{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SBI for decision-making models\n",
    "\n",
    "In [a previous tutorial](https://sbi-dev.github.io/sbi/tutorial/14_iid_data_and_permutation_invariant_embedding.md), we showed how to use SBI with trial-based iid data. Such scenarios can arise, for example, in\n",
    "models of perceptual decision making. In addition to trial-based iid data points, these models often come with\n",
    "mixed data types and varying experimental conditions. Here, we show how `sbi` can be used to\n",
    "perform inference in such models with the `MNLE` method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trial-based SBI with mixed data types\n",
    "\n",
    "In some cases, models with trial-based data additionally return data with mixed data types, e.g., continous and discrete data. For example, most computational models of decision-making have continuous reaction times and discrete choices as output. \n",
    "\n",
    "This can induce a problem when performing trial-based SBI that relies on learning a neural likelihood: It is challenging for most density estimators to handle both, continuous and discrete data at the same time. \n",
    "However, there is a recent SBI method for solving this problem, it's called __Mixed Neural Likelihood Estimation__ (MNLE). It works just like NLE, but with mixed data types. The trick is that it learns two separate density estimators, one for the discrete part of the data, and one for the continuous part, and combines the two to obtain the final neural likelihood. Crucially, the continuous density estimator is trained conditioned on the output of the discrete one, such that statistical dependencies between the discrete and continuous data (e.g., between choices and reaction times) are modeled as well. The interested reader is referred to the original paper available [here](https://elifesciences.org/articles/77220).\n",
    "\n",
    "MNLE was recently added to `sbi` (see this [PR](https://github.com/mackelab/sbi/pull/638) and also [issue](https://github.com/mackelab/sbi/issues/845)) and follows the same API as `SNLE`.\n",
    "\n",
    "In this tutorial we will show how to apply `MNLE` to mixed data, and how to deal with varying experimental conditions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Toy problem for `MNLE`\n",
    "\n",
    "To illustrate `MNLE` we set up a toy simulator that outputs mixed data and for which we know the likelihood such we can obtain reference posterior samples via MCMC.\n",
    "\n",
    "__Simulator__: To simulate mixed data we do the following\n",
    "\n",
    "- Sample reaction time from `inverse Gamma`\n",
    "- Sample choices from `Binomial`\n",
    "- Return reaction time $rt \\in (0, \\infty)$ and choice index $c \\in \\{0, 1\\}$\n",
    "\n",
    "$$\n",
    "c \\sim \\text{Binomial}(\\rho) \\\\\n",
    "rt \\sim \\text{InverseGamma}(\\alpha=2, \\beta) \\\\\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "__Prior__: The priors of the two parameters $\\rho$ and $\\beta$ are independent. We define a `Beta` prior over the probabilty parameter of the `Binomial` used in the simulator and a `Gamma` prior over the shape-parameter of the `inverse Gamma` used in the simulator:\n",
    "\n",
    "$$\n",
    "p(\\beta, \\rho) = p(\\beta) \\; p(\\rho) ; \\\\\n",
    "p(\\beta) = \\text{Gamma}(1, 0.5) \\\\\n",
    "p(\\text{probs}) = \\text{Beta}(2, 2) \n",
    "$$\n",
    "\n",
    "Because the `InverseGamma` and the `Binomial` likelihoods are well-defined we can perform MCMC on this problem and obtain reference-posterior samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torch import Tensor\n",
    "\n",
    "\n",
    "from sbi.inference import MNLE\n",
    "from pyro.distributions import InverseGamma\n",
    "from torch.distributions import Beta, Binomial, Categorical, Gamma\n",
    "from sbi.utils import MultipleIndependent\n",
    "from sbi.utils.metrics import c2st\n",
    "\n",
    "from sbi.analysis import pairplot\n",
    "from sbi.inference import MCMCPosterior\n",
    "from sbi.utils.torchutils import atleast_2d\n",
    "\n",
    "from sbi.inference.potentials.likelihood_based_potential import (\n",
    "    MixedLikelihoodBasedPotential,\n",
    ")\n",
    "from sbi.utils.conditional_density_utils import ConditionedPotential\n",
    "\n",
    "from sbi.utils import mcmc_transform\n",
    "from sbi.inference.potentials.base_potential import BasePotential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Toy simulator for mixed data\n",
    "def mixed_simulator(theta: Tensor, concentration_scaling: float = 1.0):\n",
    "    \"\"\"Returns a sample from a mixed distribution given parameters theta.\n",
    "\n",
    "    Args:\n",
    "        theta: batch of parameters, shape (batch_size, 2)\n",
    "        concentration_scaling: scaling factor for the concentration parameter of the InverseGamma distribution, mimics an experimental condition.\n",
    "\n",
    "    \"\"\"\n",
    "    beta, ps = theta[:, :1], theta[:, 1:]\n",
    "\n",
    "    choices = Binomial(probs=ps).sample()\n",
    "    rts = InverseGamma(\n",
    "        concentration=concentration_scaling * torch.ones_like(beta), rate=beta\n",
    "    ).sample()\n",
    "\n",
    "    return torch.cat((rts, choices), dim=1)\n",
    "\n",
    "\n",
    "# The potential function defines the ground truth likelihood and allows us to obtain reference posterior samples via MCMC.\n",
    "class PotentialFunctionProvider(BasePotential):\n",
    "    allow_iid_x = True  # type: ignore\n",
    "\n",
    "    def __init__(self, prior, x_o, concentration_scaling=1.0, device=\"cpu\"):\n",
    "        super().__init__(prior, x_o, device)\n",
    "        self.concentration_scaling = concentration_scaling\n",
    "\n",
    "    def __call__(self, theta, track_gradients: bool = True):\n",
    "        theta = atleast_2d(theta)\n",
    "\n",
    "        with torch.set_grad_enabled(track_gradients):\n",
    "            iid_ll = self.iid_likelihood(theta)\n",
    "\n",
    "        return iid_ll + self.prior.log_prob(theta)\n",
    "\n",
    "    def iid_likelihood(self, theta):\n",
    "        lp_choices = torch.stack(\n",
    "            [\n",
    "                Binomial(probs=th.reshape(1, -1)).log_prob(self.x_o[:, 1:])\n",
    "                for th in theta[:, 1:]\n",
    "            ],\n",
    "            dim=1,\n",
    "        )\n",
    "\n",
    "        lp_rts = torch.stack(\n",
    "            [\n",
    "                InverseGamma(\n",
    "                    concentration=self.concentration_scaling * torch.ones_like(beta_i),\n",
    "                    rate=beta_i,\n",
    "                ).log_prob(self.x_o[:, :1])\n",
    "                for beta_i in theta[:, :1]\n",
    "            ],\n",
    "            dim=1,\n",
    "        )\n",
    "\n",
    "        joint_likelihood = (lp_choices + lp_rts).squeeze()\n",
    "\n",
    "        assert joint_likelihood.shape == torch.Size([self.x_o.shape[0], theta.shape[0]])\n",
    "        return joint_likelihood.sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define independent prior.\n",
    "prior = MultipleIndependent(\n",
    "    [\n",
    "        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n",
    "        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n",
    "    ],\n",
    "    validate_args=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain reference-posterior samples via analytical likelihood and MCMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(42)\n",
    "num_trials = 10\n",
    "num_samples = 1000\n",
    "theta_o = prior.sample((1,))\n",
    "x_o = mixed_simulator(theta_o.repeat(num_trials, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 10 was passed. It will be interpreted as a batch of independent and identically\n",
      "            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n",
      "            same underlying (unknown) parameter. The resulting posterior will be with\n",
      "            respect to entire batch, i.e,. p(theta | X).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb1f53851d4a4dc0aeedd45fc0642c1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcmc_kwargs = dict(\n",
    "    num_chains=20,\n",
    "    warmup_steps=50,\n",
    "    method=\"slice_np_vectorized\",\n",
    "    init_strategy=\"proposal\",\n",
    ")\n",
    "\n",
    "true_posterior = MCMCPosterior(\n",
    "    potential_fn=PotentialFunctionProvider(prior, x_o),\n",
    "    proposal=prior,\n",
    "    theta_transform=mcmc_transform(prior, enable_transform=True),\n",
    "    **mcmc_kwargs,\n",
    ")\n",
    "true_samples = true_posterior.sample((num_samples,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train MNLE and generate samples via MCMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janbolts/qode/sbi/sbi/neural_nets/mnle.py:60: UserWarning: The mixed neural likelihood estimator assumes that x contains\n",
      "        continuous data in the first n-1 columns (e.g., reaction times) and\n",
      "        categorical data in the last column (e.g., corresponding choices). If\n",
      "        this is not the case for the passed `x` do not use this function.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Neural network successfully converged after 73 epochs."
     ]
    }
   ],
   "source": [
    "# Training data\n",
    "num_simulations = 20000\n",
    "# For training the MNLE emulator we need to define a proposal distribution, the prior is\n",
    "# a good choice.\n",
    "proposal = prior\n",
    "theta = proposal.sample((num_simulations,))\n",
    "x = mixed_simulator(theta)\n",
    "\n",
    "# Train MNLE and obtain MCMC-based posterior.\n",
    "trainer = MNLE()\n",
    "estimator = trainer.append_simulations(theta, x).train(training_batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0152d58673747fabcc4f5c51015c9f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build posterior from the trained estimator and prior.\n",
    "mnle_posterior = trainer.build_posterior(prior=prior)\n",
    "\n",
    "mnle_samples = mnle_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare MNLE and reference posterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot them in one pairplot as contours (obtained via KDE on the samples).\n",
    "fig, ax = pairplot(\n",
    "    [\n",
    "        prior.sample((1000,)),\n",
    "        true_samples,\n",
    "        mnle_samples,\n",
    "    ],\n",
    "    points=theta_o,\n",
    "    diag=\"kde\",\n",
    "    upper=\"contour\",\n",
    "    kde_offdiag=dict(bins=50),\n",
    "    kde_diag=dict(bins=100),\n",
    "    contour_offdiag=dict(levels=[0.95]),\n",
    "    points_colors=[\"k\"],\n",
    "    points_offdiag=dict(marker=\"*\", markersize=10),\n",
    "    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n",
    ")\n",
    "\n",
    "plt.sca(ax[1, 1])\n",
    "plt.legend(\n",
    "    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n",
    "    frameon=False,\n",
    "    fontsize=12,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the inferred `MNLE` posterior nicely matches the reference posterior, and how both inferred a posterior that is quite different from the prior.\n",
    "\n",
    "Because MNLE training is amortized we can obtain another posterior given a different observation with potentially a different number of trials, just by running MCMC again (without re-training `MNLE`):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat inference with different `x_o` that contains more trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 50 was passed. It will be interpreted as a batch of independent and identically\n",
      "            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n",
      "            same underlying (unknown) parameter. The resulting posterior will be with\n",
      "            respect to entire batch, i.e,. p(theta | X).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45ed8a0ba0244d3097b90da70d9dceb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd5d42d72927422f850b36b80cdea2f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_trials = 50\n",
    "x_o = mixed_simulator(theta_o.repeat(num_trials, 1))\n",
    "true_samples = true_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)\n",
    "mnle_samples = mnle_posterior.sample((num_samples,), x=x_o, **mcmc_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot them in one pairplot as contours (obtained via KDE on the samples).\n",
    "fig, ax = pairplot(\n",
    "    [\n",
    "        prior.sample((1000,)),\n",
    "        true_samples,\n",
    "        mnle_samples,\n",
    "    ],\n",
    "    points=theta_o,\n",
    "    diag=\"kde\",\n",
    "    upper=\"contour\",\n",
    "    kde_offdiag=dict(bins=50),\n",
    "    kde_diag=dict(bins=100),\n",
    "    contour_offdiag=dict(levels=[0.95]),\n",
    "    points_colors=[\"k\"],\n",
    "    points_offdiag=dict(marker=\"*\", markersize=10),\n",
    "    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n",
    ")\n",
    "\n",
    "plt.sca(ax[1, 1])\n",
    "plt.legend(\n",
    "    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n",
    "    frameon=False,\n",
    "    fontsize=12,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.5565)\n"
     ]
    }
   ],
   "source": [
    "print(c2st(true_samples, mnle_samples)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we can see that the posteriors match nicely. In addition, we observe that the posterior's (epistemic) uncertainty reduces as we increase the number of trials. \n",
    "\n",
    "Note: `MNLE` is trained on single-trial data. Theoretically, density estimation is perfectly accurate only in the limit of infinite training data. Thus, training with a finite amount of training data naturally induces a small bias in the density estimator. \n",
    "As we observed above, this bias is so small that we don't really notice it, e.g., the `c2st` scores were close to 0.5. \n",
    "However, when we increase the number of trials in `x_o` dramatically (on the order of 1000s) the small bias can accumulate over the trials and inference with `MNLE` can become less accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNLE with experimental conditions\n",
    "\n",
    "In the perceptual decision-making research it is common to design experiments with varying experimental decisions, e.g., to vary the difficulty of the task. \n",
    "During parameter inference, it can be beneficial to incorporate the experimental conditions. \n",
    "In MNLE, we are learning an emulator that should be able to generate synthetic experimental data including reaction times and choices given different experimental conditions. \n",
    "Thus, to make MNLE work with experimental conditions, we need to include them in the training process, i.e., treat them like auxiliary parameters of the simulator: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a simulator wrapper in which the experimental condition are contained in theta and passed to the simulator.\n",
    "def sim_wrapper(theta):\n",
    "    # simulate with experiment conditions\n",
    "    return mixed_simulator(\n",
    "        theta=theta[:, :2],\n",
    "        concentration_scaling=theta[:, 2:]\n",
    "        + 1,  # add 1 to deal with 0 values from Categorical distribution\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a proposal that contains both, priors for the parameters and a discrte prior over experimental conditions.\n",
    "proposal = MultipleIndependent(\n",
    "    [\n",
    "        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n",
    "        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n",
    "        Categorical(probs=torch.ones(1, 3)),\n",
    "    ],\n",
    "    validate_args=False,\n",
    ")\n",
    "\n",
    "# Simulated data\n",
    "num_simulations = 10000\n",
    "num_samples = 1000\n",
    "theta = proposal.sample((num_simulations,))\n",
    "x = sim_wrapper(theta)\n",
    "assert x.shape == (num_simulations, 2)\n",
    "\n",
    "# simulate observed data and define ground truth parameters\n",
    "num_trials = 10\n",
    "theta_o = proposal.sample((1,))\n",
    "theta_o[0, 2] = 2.0  # set condition to 2 as in original simulator.\n",
    "x_o = sim_wrapper(theta_o.repeat(num_trials, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtain ground truth posterior via MCMC\n",
    "\n",
    "We obtain a ground-truth posterior via MCMC by using the PotentialFunctionProvider.\n",
    "\n",
    "For that, we first the define the actual prior, i.e., the distribution over the parameter we want to infer (not the proposal).\n",
    "\n",
    "Thus, we leave out the discrete prior over experimental conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 10 was passed. It will be interpreted as a batch of independent and identically\n",
      "            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n",
      "            same underlying (unknown) parameter. The resulting posterior will be with\n",
      "            respect to entire batch, i.e,. p(theta | X).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99b53f79862c4464a152a809161b0a26",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior = MultipleIndependent(\n",
    "    [\n",
    "        Gamma(torch.tensor([1.0]), torch.tensor([0.5])),\n",
    "        Beta(torch.tensor([2.0]), torch.tensor([2.0])),\n",
    "    ],\n",
    "    validate_args=False,\n",
    ")\n",
    "prior_transform = mcmc_transform(prior)\n",
    "\n",
    "# We can now use the PotentialFunctionProvider to obtain a ground-truth posterior via MCMC.\n",
    "true_posterior_samples = MCMCPosterior(\n",
    "    PotentialFunctionProvider(\n",
    "        prior,\n",
    "        x_o,\n",
    "        concentration_scaling=float(theta_o[0, 2])\n",
    "        + 1.0,  # add one because the sim_wrapper adds one (see above)\n",
    "    ),\n",
    "    theta_transform=prior_transform,\n",
    "    proposal=prior,\n",
    "    **mcmc_kwargs,\n",
    ").sample((num_samples,), show_progress_bars=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train MNLE including experimental conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janbolts/qode/sbi/sbi/neural_nets/mnle.py:60: UserWarning: The mixed neural likelihood estimator assumes that x contains\n",
      "        continuous data in the first n-1 columns (e.g., reaction times) and\n",
      "        categorical data in the last column (e.g., corresponding choices). If\n",
      "        this is not the case for the passed `x` do not use this function.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Neural network successfully converged after 73 epochs."
     ]
    }
   ],
   "source": [
    "trainer = MNLE(proposal)\n",
    "estimator = trainer.append_simulations(theta, x).train(training_batch_size=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct conditional potential function\n",
    "\n",
    "To obtain posterior samples conditioned on a particular experimental condition (and on x_o), we need to construct a corresponding potential function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56b55012d9e44c248ebeff1e17d259ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 20 chains:   0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, we define the potential function for the complete, unconditional MNLE-likelihood.\n",
    "potential_fn = MixedLikelihoodBasedPotential(estimator, proposal, x_o)\n",
    "\n",
    "# Then we use the potential to construct the conditional potential function.\n",
    "# Here, we tell the constructor to condition on the last dimension (index 2) by passing dims_to_sample=[0, 1].\n",
    "conditioned_potential_fn = ConditionedPotential(\n",
    "    potential_fn,\n",
    "    condition=theta_o,\n",
    "    dims_to_sample=[0, 1],\n",
    "    allow_iid_x=True,  # we also need to explicitly tell that MNLE allows iid_x\n",
    ")\n",
    "\n",
    "# Using this potential function, we can now obtain conditional samples.\n",
    "mnle_posterior = MCMCPosterior(\n",
    "    potential_fn=conditioned_potential_fn,\n",
    "    theta_transform=prior_transform,\n",
    "    proposal=prior,\n",
    "    **mcmc_kwargs\n",
    ")\n",
    "conditional_samples = mnle_posterior.sample((num_samples,), x=x_o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3phdoxjNLtD41T1c9v0wvzR7LMqpWolnN4KXu0prHznpmNDz0aAAAAPjct1szdMPLq1ThNnXW0E7614XDZA/wkNEQ/RKj9PLscYoNd2rNvlxdPW+5Csv4+bM1aFbQKK86jM/l8ciw1zOjQdAAAADwqUXbMvXrl1aq3O3RtMGJevTi4XLYm725aMAY2DlaL88ep+hQR83p4sXl/Azqb836F1i9dMplmlI929tW0gwOAADgM4t3ZOmaF1aovNKjUwcm6PEZI4IqZFQbnBSjl2aPU5TLoWW7czR7/gqVlHMGhz81q0ejZkbDNKX6msGZ0QAAAGiyknJ3vb+d35SWr1+/uFJllR6d1L+jnrx0hJxBGDKqVZ8ufuVzy/TTzmz9+qUVeqiJS8iiw5xB/XdphWYFjeoejRBT9QYNj4ckCQAA0BRvrdin2z/YoNKKox9Yd2LfeP3nspFyOepeYRJMqk8Xv/L5ZVq0LUtj7/m6SeMkRLv03FVjNDgpxuIKg4c1PRpHmdHgwD4AAIDGe3dViv74zrpjhoxpgxP1vytGKdRJyKhWfbp4YnRok8c4kF+my59bqs1p+RZWFlws2d6WpVMAAADW+WBNaq1Tve8+J/AO3PO38T3ba8lfT27SewtKK3T5c8u0dl+uLnt2qV7/9Xj1TYiyuMK2r3nN4JXHbgZne1sAAICG+2Rdmv7vzbUBfap3oIsKderFWWM1JClGOUXluvSZpdqeUejvsgKOhbtOMaMBAADQHG3pVO9AFxPm1Euzx2pgp2hlFZbp0meWaFdWkb/LCiiWBI0QggYAAECztMVTvQNdbHiIXr5mnPonRimjoEwz/rdEe7IJGw1lSTN46NGChnn0BiYAAIBgF6ynegeCuAhv2OjTMVLp+aWa8b8l2pdT7O+yAoJF29vW36PhNpnRAAAAdcsoKNW9n25Ram6Jv0vxqzX7cmtO9X4kyE71DgQdIl165dpxuuR/S7Qzs0gznlmiN647TkmxYf4urVVrgQP7OEcDAAAcKauwTJc9s1TbaLKVJJ06MEGPXRLcB+61Zh2jQvXateN18dM/aXd2sS59Zole//V4dYohbNTHmu1tPUdbOsWMBgAAqC2nqFyXP+sNGYnRofrLGf2D+gfs6FCnjuvVnuVSrVxCdKhe+/V4Xfz0Eu3JLtalz3i3vk1oxnkdbZnPZzQ8JjMaAADgkNxib8jYkl6gjlEuvfbr8UruEOHvsoAG6RQTplevHaeLn/buQuWd2ThO8VEuf5fW6jTrVwcN6tFg1ykAAFAlr6RCVzy3TJvS8tUh0qVXryVkIPB0aReu1389Xp1jQrUj0xs2sgvL/F1Wq2PJrlNHm9GopEcDAABIyi+t0JXPL9P61Dy1jwjRa9eOU++Okf4uC2iSrnHhevXa8UqIdmlbRqEue3apDhaV+7usVsXnB/Z5ZDbnFgAAoA0oLKvU1c8v09p9uWoX7tQr145Tn4Qof5cFNEuPDhF67drxio9yaUt6gS57dqlyiwkb1axpBufAPgAAWlRecYU+25Cm0orAWDnw8bo0rdqbq5gwZ9UBaNH+LgmwRM/4SL1WtfXtprR8Xfn8Mr00e5xiwpz+Ls3vLAwadfdoVNIMDgCApbIKy3TJ/5Zoe4BtCxsV6tDLs8dpUOcYf5cCWKp3xyi9cs14zXhmidal5Omq55fppdljFRUa3GHDkqARctRdpzgZHAAAq1RvC7s9o1Ado1wamxzn75IaJMxp19XH9yBkoM3qlxill2eP06XPLtGafbm6et5yvTBrrCJdzfpxO6BZsr1t6NGWTjGjAQCAJX65Lewb1x3Hjk1AKzKwc7Q3bDyzRCv3HNSsecs1f9YYhYcEZ9ho3va2lYdvb1tfjwYzGgAANFdecYUuf25pzbawnD0BtE6Dk2L00uxxinI5tGx3jmbPX6GS8uD8xbvPt7flwD4AAJrHuy3sUm1Iza/ZFrZXPNvCAq3VsK6xemG2d9nUTzuz9euXVgTMxg1WsnB72/qawZnRAACgqWq2hU3JY1tYIICM7NZO82eOUXiIXYu2Zem6l1aqrDK4woYlPRpH3d6WoAEACHJllW7lFVc0+n3lbo9ufmMN28ICAWp0jzjNu3qMrp63XN/9nKnfvLxK/zx3iGxG3dfHR7lkGPW8GICaFTRK3cfu0fCYHNgHAAhehWWVmvzgt8oqLGvyGGwLCwSucT3b67mrRmvm/OX6ekuGvr7363qvHdY1VvOvHqN2ESEtWKHv+LxHo1LMaAAAgte+nOKakGG3GY3+0zUuTC/NHqchXQgZQKCa0LuDnr1qtBKjQ+v9b12S1u7L1eXPLW3SDGhr1OQZDdM0D/VoeOrv0WB7WwAApA6RLq342yn+LgOAn0zsE68lfz253te3HSjQjGeWaOP+fF3x/NI2cbp4k2c0Kj2VMuVdFnX0XadYOgUAAAAcTZ8E7+nicREhNaeLF5QG9sxGk4NGdX+GdIxmcDGjAQAAABxL9eniseFOrdmXq5nzlquorNLfZTVZk4NG9bIpw5Sc0lGCBjMaAAAAQENUny4eHerQij0HNXP+chWXB2bYaHLQqGkEl2RIbG8LAAAAWKDW6eK7cnTNC4F5unizZzRCqnsw6m0GZ0YDABC83B7+7yCAxhvWNVbzZ41VRIhdi3cE5unizQ4arur//ax36RQzGgCA4FRQWqHbP9ggSeoUE+rnagAEmlHd22n+rLE1p4tf/3JgnS5u4YwGPRoAAFQrKqvUzHnLtbrqVO/7zh/i75IABKAxPeL0/NVjFOq06dut3tPFyysD4xf5ze7RCK3uwXDW/Zsalk4BAIJNcXmlZs1frhV7DnKqN4BmG9+zvZ6/aoxcDpu+3pKh3762ShXu1h82mr69baV3e9sQT9UXGRJV53UeZjQAAEGktMKta15YoaW7chTlcnCqNwBLTOjdQc9cOVohDpu+2HhAN72+RpWtPGw0f9ep6hkLV2Sd17HrFAAgWJRWuHXtiyu0eEe2IkLsmj9rrIZ3jfV3WQDaiBP7xuvpy0cpxG7TJ+vT9H9vrm3VG05Y0AxuSjan5HDVeV3gtKsAANB0ZZVu3fDySi3alqUwp13zZo7VqO7t/F0WgDZmSv+O+s9lI+W0G/pw7X7d+lbrDRvWBI16ZjMkmsEBAG1feaVHN76yWgu3ZirUadPzV4/R2OQ4f5cFoI06ZWCCnpgxUnaboXdXp+rP76yTpxWGDWuCRj39GRIzGgCAtq3C7dHvXlutBZsPyOWw6dkrx+i4Xu39XRaANu70wYl6/JIRstsMvbUyRbe9v77VhQ1rtrdlRgMAEIQq3R7d9MYafb4xXSF2m56+YpRO6NPB32UBCBJnDu2khy8aJpshvbZsn+74cIPMVrTjqwXb25pSyFGCRuv5WgEAsIzbY+qWt9bqk3VpctoN/feKkZrcr6O/ywIQZM4enqR/XThMhiG9vGSv/v7RplYTNpo/o+E5xoyG0Tq+UAAArOLxmPrj2+v0wZr9ctgM/eeyUTqpf4K/ywIQpM4b2UX3nzdUkjR/8W7d8+nmVhE2LOrRONrSKQAA2g6Px9Rf3l2vd1alyG4z9MSMETp1ICEDgH9dNKar7jl3iCTpmUW79MAXW/0eNizq0ai/GZwD+wAAbYVpmrr9gw16Y8U+2Qzp0YuHa9qQTv4uCwAkSZeO66a5Zw+SJD317Q498tXPfq3H9z0aTb0BAACtiGmauuvDjXpl6V4ZhvTwRcM1fVhnf5cFALVceVwP3XHWQEnS499s1+Nfb/NbLT7fdaqyqTcAAKCVME1Td3+yWS/8tEeGIT14wTCdMyLJ32UBQJ1mnZCsv57RX5L08Fc/a+GWDL/U4fMeDU9TbwAAQCtgmqbu+2yLnvthlyTpvvOG6IJRXfxcFQAc3a9P7KVpgxMlSZvS8v1Sg0Ung3NgHwCg7TFNU//6cque/n6nJOmf5w7WxWO6+bkqAGiY6FCnX+/v8xmNSqOpdwAAwL8eXbBN/164Q5I09+xBumxcdz9XBACBo+lBo/LwGQ2WTgEA2pYnvt6mx6qaKG8/a6CuPK6HfwsCgADj++1tDcPve/gCANAYT327Qw9VbQv5l2n9NfuEZD9XBACBx6LtbesPGpLkNunUAAAEhmcX7dT9n2+RJN06tZ+um9TLzxUBQGDy+fa2kuTxEDQAAK3f/B936e5PNkuSbjqlj26c0tvPFQFA87k9/lld5PNmcEmqrJr9AACgtXppyR7d9dEmSdKcKb31+5P7+LkiAGiexJhQSd6Z2g2peS1+/xaZ0XBXNY4DANAavbZsr25/f4Mk6fpJvXTLaX1lGGybCCCw/frEnhrdvZ3ySyt12bNLtWl/y56n0YweDW94CG3AjIbHw4wGAKB1emvFPv31vfWSpGtOSNafTu9HyADQJkS4HJo/a6xGdItVXkmFLn9uqbamF7TY/ZsUNEzTVFnVcqgQe6hksx/1epZOAQBao/dWp+iP76yTaUpXT+ih284cQMgA0KZEuhx6YdZYDesSo5yicl327BJtz2iZsNGkoFHhqZApb1OJyxlxzOvdBA0AQCvzwZpU3fLmWpmmdPn4brpz+kBCBoA2KTrUqRdnjdPgpGhlFZZrxjNLtSOz0Of3bVLQqO7PkKTQkAYEDU9FU24DAIBPfLIuTf/35lp5TGnG2K6a+6vBhAwAbVpMuFMvzRqnAZ2ilVlQpkufWaLdWUU+vWezgoZhmnIcoz9DYkYDANB6fLExXb9/fbXcHlMXjuqif54zRDYbIQNA29cuIkSvXDNO/RKidCC/TDOeWaK92cU+u1+Tgkb1YX0u05Thij7m9W43MxoAAP/7evMBzXl1lSo9ps4bkaT7zh9KyAAQVOIiQvTKtePUu2Ok0vJKNeOZJUo56Juw0aSgUeoulVS1tS0zGgCAAPDt1gzd8PIqVbhNTR/WWQ9cMFR2QgaAINQh0qVXrx2nnh0ilJpbohnPLNH+3BLL79OsGY3QBpyhIdGjAQDwr0XbMvXrl1aq3O3RGUMS9chFw+SwN3mHdwAIeB2jQvXqtePVvX249uWU6NJnlig9r9TSezSrR6PhMxoEDQCAfyzenqVrXlih8kqPThuYoMcuGUHIAAB5Tw5/7drx6hoXpt3Zxbr0mSXKyLcubDQtaFSd9O0yTckVdczrmdEAAPjD0p3Zmv3CCpVVenRy/4568tKRchIyAKBG59gwvXrNeCXFhmlnVpEufXapMgvKjv3GBmjmjIYaNqNB0AAAtLAVu3M0c/5ylVS4NalvvP5z+UiFOAgZAPBLXePC9dq149UpJlTbMwp1+bNLlVPU/B7rZvZoeBrWo8HSKQBAC1q196CunrdcxeVundC7g56+YpRcDru/ywKAVqtb+3C9eu14dYxyaeuBAl327FLlFjcvbDR/RqMBS6cqmdEAALSQ0gq3rnlhhQrLKnVcz/Z65srRCnUSMgDgWJI7ROi1X49Xh0iXNqfl6y/vrm/WeM0KGq4GNoN73JVNuQ0AAI2WWVCmnKJyhdhteu7q0QoLIWQAQEP1io/UgxcMlSRtSS9o1ljNDxoNmNEoc1u7VRYAAMditxkKD3H4uwwACDhRodb8b2eLzGgcKM1pym0AAAAABKhmNYO7Gnhg34HS7KbcBgAAAECAalLQKK1aChXiadiMRnoZMxoAAABAMGnajEbVgX2hDezROFCc0ZTbAAAAAAhQTevRqCiSJIXQowEAaGUqPaa/SwCANqHC7ZFpNv1/U5s2o1EVNFyS5Aw75vXplYXNKhIAgIYoLq/Un95eJ0nqFBvq52oAIDAlxnj/9zPlYIke/GJrk3+Ob1qPRnnVjIYtRDKMY15fIo8KKpq3Dy8AAEdTUu7WrPnLtWx3jqJcDj1y0XB/lwQAAalLu3D9/VeDJEn/+XaHHlmwrUnjNLFHo1iSFGp3HfPaGLdbkpRelN6UWwEAcEylFW5d++IKLdmZo0iXQy/MHqthXWP9XRYABKyrJvTQ7WcNlCQ9/vU2PfF148NG03o0Kqt2naojaOSXViglp7jm84RKb9A4QNAAAPhAaYVb1720Uj9sz1J4iF3zZ47RyG7t/F0WAAS82Sck6y/T+kuSHvrqZz317Y5Gvb9Jx/6VVZZIklxO7/qt0gq3vtiYrrdXpuiH7Vlylx06CTy+0q3tku75aIFGdWqnE3p30OR+HRXqtDfl1gAA1Civ9Og3r6zSdz9nKsxp17yrx2h0jzh/lwUAbcZ1k3qp0mPqwS+26v7Pt8hhM3TtiT0b9N6mBY2qk8FD7GG6//MtennJHhWUVta8Huo8NFES4/YGioLiPXp5yV69vGSvIl0OnTYwQdOHddbEPh3ksDdpYgUAEOTmvLpK32zJkMth03NXjda4nu39XRIAtDk3TumtCrdHjy7Ypn9+ull2m6FZJyQf831NDBrek8F3ZlXqqS3eKZSk2DCdP6qLzh+ZpA6hUuQD3mu7RbSTVKIhXcp0Wt9kfb4hTfvzSvXu6lS9uzpVidGhumhMV108pquSYo+9gxUAANW+3HRAIQ6bnr1qtCb07uDvcgCgzfr9yX3k9ph64pvtmvvxJt8EDdM0lV+1NCq/3KG4iBDdc+5gnTYwUTabdweqoqKimusTQttLSlGlLVd3TB+ov505QKv3HdSHa/bro3VpSs8v9TaYfLNNk/vG69Jx3TWlXzyzHACAYwp12vTU5aM0sU+8v0sBgDbNMAz936l9VeE29fbKlIa9x2zExrimaeqfn2zWp+lXqMBZrt/l9NavZr+mhOjae5UXFRUpMtJ7kN+3b87SnOJlSrZH6MPLl9S6rqzSrS83HtBry/Zq8Y7smuc7xYTq4qpZjk4xzHIAAOqWUVCqjlGclwEALcU0TWUVlis+6ti7zzZ4RsM0Tf3j4816/sddSuzjkSSd0K/7ESHjlzrG9pSKlyndXSLTNGUcdu6Gy2HX9GGdNX1YZ+3KKtLry/bqzRX7lJZXqkcXbNPjX2/TSf07asbYbprUl1kOAEBthAwAaFmGYTQoZEiNCBpzP96keT/u9t7A7p0ECQ2JOub7Osb1kfYfOrQvOiS6zuuSO0ToL2cM0M2n9tUXG9P1ytK9WrYrRws2Z2jB5gwlRofq/FFJunBUV/XoENHQsgEAAAD4QYOnCKpDxj3nDlGl4Z3RcIXWHRoOF9quu2IbcWhfqNOus4cn6c3rjtOC/5ukaycmq124U+n5pfr3wh2a/K9vddF/f9Kby/epoLSioeUDAAAAaEGNWot073lDNGNsV5XKO6MREhJz7DdFd1Fi9aF9eXsbVVzvjpG67cyBWvLXk/Wfy0Zqcr942Qxp2e4c/fGddRp99wL97rXVWrg1QxVuT6PGBgAAAOA7DV46dd95Q3TJ2G4qr9raVpJCwxoQNEJjlOCRtkhKz94i9Til0UW6HHadMaSTzhjSSWl5JXp3VareXZWiHZlF+nDtfn24dr/ahTt1+uBEnTW0s8Ylx9HPAQAAAPhRg4PGJWO7STp0WJ8kuVyxx36jYSjBES6pUgdydzW2viN0ignTjVN66zeTe2ldSp7eXZWiT9anKauwXK8t26fXlu1Tu3CnTuqfoFMHJujEvh0UHtKk40IAAAAANFGjfwKvDhqGacoRGtug9yS6YiVPlg4U7m/s7eplGIaGdY3VsK6xuv2sgVq6K0cfr9uvzzak62Bxhd5ZlaJ3VqUoxGHT2B5xmting07sG6/+iVG1dr4CAAAAYL0mB41Q05ThOvauU5KUEN5RKsxSeklmY2/XIA67Tcf37qDje3fQP84erOW7D+qrTQf01eZ07csp0Q/bs/TD9izd+9kWxUWEaGyPOI1N9v7pnxjFMisAAADAYk0OGiGmKbkiG/SexKhuUuEmHajIb+ztGs1ht+m4Xu11XK/2uv2sAdqRWaTvf87Uom2ZWrIzRzlF5fp8Y7o+3+jdASvMadfQLjEa0a2dhneN1fCusUqMYV92AAAAoDkaHTTKy0skSS7TlEIaFjQSYntJaVK6u/SIQ/t8yTAM9e4Yqd4dIzXrhGSVVbq1ITVPS3flaNmuHK3cfVAFZZVauitHS3fl1LyvY5RLQ7vEanjXGA3tEquhXWIUGx7SIjUDAAAAbUGjg0ZpqfcHcu+MRgOXTnUYIEkqMcyjHtrnay6HXaO6x2lU9zj9ZrLk8ZjakVmo1XtztXrfQa3Zl6efDxQoo6BMCzYf0ILNB2re26N9uIZ28faEDOsSo0GdYxQWYvfL1wEAAAC0do2f0SjNkySFmoZkdzboPaHteynW7Vau3a70gv2Kbu+foPFLNpuhPglR6pMQpYvGdJUklZS7tXF/ntbsy9W6lDytS8nV7uzimj8frvU2tNsMqU/HKA1OitHgpGgN6BStAYnRiglv2N8JAAAA0JY1vkejzBs0Qhqz/CkyUQlVQeNA9lb1bd+/sbdtMWEhdo3uEafRPeJqnsstLte6lDyt3ZertSm5WpuSp8yCMm09UKCtBwr0zqpD70+KDVPvjpHqFR+pnvER6hkfoW5x4eoUEya7jd2uAAAAEBya0KNRIElyGY1YNmR3KFEh2irpQM7Pjb2l38WGh+jEvvE6sW98zXMH8ku1LiVP61PztGl/vjan5Ss1t6Tmz3c/195hy2EzlNQuTJ1jwtQpNlSdYkKVGBOmjlEuxUe51DHKpQ6RLoU6WY4FAACAwNf4Ho0y785RIY0JGpISHBGSipSet7uxt2yVEqJDderAUJ06MKHmufzSCm1NL9COjELtyCzUjswi7cwsVGpuiSrcpvZkF2tPdvFRx410ORQXEaL2kSGKCw9Ru4gQtQt3ql1EiGLDvI9jw0PULsKp2LAQxYY7CScAAABodZowo1EoSQo1GvfWxNA4qbxIB4rSGnvLgBEd6tSYHnEac9iyK0lye0wdyC/V3pxipeWVKC2vVOl5pUrLK1VmQVnNn3K3R4VllSosq9TenKMHksOFOm2KCfMGj5hwp6JDnYoJ8/6JDnMoKtSpqFCHokMdinB5/0S6HAoPsSs8xPvR5bBxkCEAAAAs0/gejfIiSVKIrXFNzwkRiVL5Pm0vOaD88ny/7TzlD3aboc6xYeocG1bvNaZpqqCsUtmF5couLFN2UbkOFpUrp7hcOYXlyi2pUG5xuQ4WV+hgcbnyiiuUW1Iht8dUaYVHpRVlOpBf1uQaDcN7pkio065Qh02hTrtcTrtCnTa5HDa5HNWPvaHE5bQp1GGXq+q5sOprnd7HYU67wkIOvSfUedj7nTaF2G2EGwAAgDaswUHj80X/kClTazPXSJJc9sadK9E1Nlk6uFwbKvM1+fUTdUJ0b02J7a9wu6tR4wSTdpLa2aVeMZJi6r6m3O1RWYVHZZXVf9wqr3pcXv2521RFpVsVlabK3R5VuD2qcJuqcHvk9phHDmpKKq/6I8ktqbjqj9VshiHD8O7iZRiGDHk/lyEdHkHMmv+vDlXXGlWPbVVP2GTIZjt0n+p7VV9vO+wmRs04RtX9TFX9vwYzpJrg5P16vONV39ewGbLV1OJ93WazeWuxeesxqv4ebJJk876/etxDNdau2ftJ1Xt+8Zpx2N+i8Yu/0+rn6nvCighY8/dnHvqbNM26r6nre2xWP3Gsb8QvvtZDzx3ja/7FX0Bzv+a6vt46PpWpxn29v7vwgWZWBgBAy2tw0Lh155u1Pg+3N+707GGdxukPK57W+1ER2h4SooV5W7Uwb2ujxkAT2Kr+sOtu6+bxdwFozX4nggYAIPA0OGiMMV01vx0MMxy6aORvGnUjW/JkXdXrHF2Vu0fbzDJ9rmKtUSk/XwUp87DpgsbMGhx9zF8++OWnR77QlHv/8rfexxqjrrrMuiqrt+5jD37ktVb9rdZ9S/8seGvs37x1fnmnlvn6WVYIAAhshmn+clK/+YqKihQZGSlJKiwsVEREhNW3AAAAANCK2Y59CQAAAAA0DkEDAAAAgOUIGgAAAAAsR9AAAAAAYDmCBgAAAADLETQAAAAAWI6gAQAAAMByBA0AAAAAliNoAAAAALAcQQMAAACNtnjxYo0fP16LFy/2dylopQgaAAAAaLQnnnhCS5cu1ZNPPunvUtBKGaZpmlYPWlRUpMjISElSYWGhIiIirL4FAAAA/CQrK0udOnVSZWWlHA6H0tLS1KFDB3+XhVaGGQ0AAAA0ygsvvCCPxyNJ8ng8evHFF/1cEVojZjQAAACq7MzbqX8t/5cmd52si/pd1PA3uiulr+6QdnzT6Ht+Z6/U0yEVKpHlP5JZouRghcryK2s9t+ypPSrOrKj5PDw+RGNv6FbrGle0Q2HtnC1So084XFJUJ8lmt2S47bnbmz3GuivXyTAMC6ppGT4JGqZpqri4WJIUHh4eUH8hAAAgOO3J36OZn89UZkmmJOnW0bfqykFXHvuN7krpvV9LG95p9D2/CQ/TLR07qLIV/6y0856dKv65uNHvC+8brp5/7emDioLX0kuXKtwZ7u8yGszhi0ENw2AWAwAABIx9+fs064tZyizJVFxonHJKc/Tgigdlt9l12YDL6n+jxy198BtvyLA5pTMfkuKSG3TP77M36JZN/1Ol6da0+NG6oNPxFn011vr08sW69+6XVFFRqYb8etowJKfToVsvv1hnDJ3g+wJ9obxYWvy4VJYvxfeXTvm7FNK0n2135+/WP5b8o+bza4dcK5vR+O6FU7ufGlAhQ/LRjAYAAECgSC1M1czPZyqtKE29YnrpuanP6ZXNr+iZ9c9Ikm4ff3vdy6g8HunDOdKaVySbQ7rwBWnAWQ2654+pP+q33/xWFZ4Knd7jdN078V45bD75/a8lNm3apHPPPVfbt2+v6c2oi81mU58+ffTuu+9q4MCBLVihD6Svl16YLpUclLqOly5/R3JFNmqIvfl7NfPzmcooyVDfdn313GnPKTY01jf1tkI0gwMAgKCVVpim2V/MVlpRmnpE99CzU59V+7D2+u2I32rm4JmSpH8s+Yfe+fkXy6I8Hunj33tDhmGXzn+uwSHjp/0/6fcLf68KT4VO7X6q7pl4T6sOGZI0cOBArVq1ShdeeOFRr7vooou0atWqwA8ZkpQ4RLrifSk0Rtq3RHr1Iqm8qMFvTylI0ewvZyujJEO9Y3vrmdOeCaqQIRE0AABAkEovStesL2YptTBV3aO767mpz6lDmHeLVsMwdPPIm3XFwCskSX//6e96f/v73jeapvTpH6RVL0qGTTrvf9Kgcxp0z+Xpy/W7b36nMneZJnedrPsn3i+nLTAapiMiIjRp0qR6e28Nw9CkSZMUHh5Yy3uOqvNw6Yr3JFe0tOdH6bVLvMuqjmF/4X7N/mK20ovSlRyTrGdOe0ZxoXG+r7eVIWgAAICgk1GcoWu+vEYphSnqEtlFz572rDqGd6x1jWEYunX0rbq0/6UyZeqOH+/QRzs+kj77k7TiOUmGdM5/pSEXNOieKw+s1I1f36hSd6kmJk3UQ5MektMeGCGj2sqVK2W3170Lk91u18qVK1u4ohaQNMq7bCokUtr1vfT6pVJFab2XVwfY/UX7vQH2tEMBNtjQowEAAFqtu5fcrTe2vuHTe5xdUKjOle56Xzcl/bddjKX3nBU9UK4A/H3v3b//TGn78mSzGbLZDZ14eh99//k2edymPB5TnbrG6G+PTfN3mb6RnyId3HPMyz6JDNdep1NdKyo0Ly1DCe76/201yphrvJsNBBCCBgAAaJWKK4o17tVx/i4DVTzlHm26fpPkkUISQ9Ttt90UmhSq0tRS7X1ir8rTyyWbNPC/A2ULCbwQZaWkikrNSzugTlaFjGp/3d/k3a/8oXV3HgEAgKAV7gzXuE7jtDRtqU/Gn1RcosTKymNfWMWU9FZUpMxmnHlxtquzQg1rDoBraSWVFcrpuk+dkqN11sxBCnFVfR29pPL7kvXxvE1K252vc+ydFRYaWEvCGqUoSyrKqPflSI9Hl+QXKtHqkNFzSkCFDIkZDQAAADSQx+ORzVb/bMWxXkdwIWgAAAAAsByREwAAAIDlCBoAAAAALEfQAAAAAGA5ggYAAAAAyxE0AAAAAFiOoAEAAADAcgQNAAAAAJYjaAAAAACwHEEDAAAAgOUIGgAAAAAs52jIRaZpqqCgwNe1AADqERUVJcMw/F0GAAAN1qCgUVBQoJiYGF/XAgCoR15enqKjo/1dBgAADWaYpmke66K2MqORn5+vrl27at++fQH9f7D5OloXvo7WpS18HXV9DcxoAAACTYNmNAzDCNj/g12X6OjoNvH18HW0LnwdrUtb+DrawtcAAAheNIMDAAAAsBxBAwAAAIDlgipouFwu3XnnnXK5XP4upVn4OloXvo7WpS18HW3hawAAoEHN4AAAAADQGEE1owEAAACgZRA0AAAAAFiOoAEAAADAcgQNAAAAAJZrM0Hj3nvv1ZgxYxQVFaWOHTvqnHPO0datW4/6nvnz58swjFp/QkNDW6jixrvrrruOqLd///7+LqtOPXr0OKJWwzB044031nl9a/9efP/995o+fbo6d+4swzD0/vvv13rdNE3dcccd6tSpk8LCwnTKKado27Zt/ilWR6+3oqJCf/rTnzRkyBBFRESoc+fOuvLKK7V///6jjtma/v0d6/tx9dVXH1Hr6aef7pdapWPXW9d/K4Zh6MEHH6x3zNb0/QAAoC5tJmh89913uvHGG7VkyRJ99dVXqqio0GmnnaaioqKjvi86OlppaWk1f/bs2dNCFTfNoEGDatX7ww8/+LukOi1fvrxWnV999ZUk6cILL6z3Pa35e1FUVKRhw4bp3//+d52vP/DAA3r88cf13//+V0uXLlVERISmTp2q0tLSFq7U62j1FhcXa9WqVbr99tu1atUqvfvuu9q6dat+9atfHXPc1vLv71jfD0k6/fTTa9X62muvtWCFtR2r3sPrTEtL0/PPPy/DMHT++ecfddzW8v0AAKAuDn8XYJXPP/+81ufz589Xx44dtXLlSp144on1vs8wDCUmJvq6PMs4HI6AqDc+Pr7W5/fdd5969eqlSZMm1fue1vy9mDZtmqZNm1bna6Zp6tFHH9Xf/vY3nX322ZKkF198UQkJCXr//fd1ySWXtGSpko5eb0xMTE3wq/bkk09q7Nix2rt3r7p161bvuK3l39/Rvr5qLperVdQqHbveX9b5wQcfaMqUKerZs+dRx20t3w8AAOrSZmY0fikvL0+SFBcXd9TrCgsL1b17d3Xt2lVnn322Nm7c2BLlNdm2bdvUuXNn9ezZU5dddpn27t3r75KOqby8XC+//LJmzZolwzDqvS7QvhfVdu3apfT0dJ1yyik1z8XExGjcuHH66aef/FhZw+Xl5ckwDMXGxh71ukD69/ftt9+qY8eO6tevn2644QZlZ2f7u6QGOXDggD755BPNnj37mNcG0vcDABB82mTQ8Hg8uummm3T88cdr8ODB9V7Xr18/Pf/88/rggw/08ssvy+PxaMKECUpJSWnBahtu3Lhxmj9/vj7//HM99dRT2rVrlyZOnKiCggJ/l3ZU77//vnJzc3X11VfXe02gfS8Ol56eLklKSEio9XxCQkLNa61ZaWmp/vSnP2nGjBmKjo6u97pA+vd3+umn68UXX9TXX3+t+++/X999952mTZsmt9vt79KO6YUXXlBUVJTOO++8o14XSN8PAECQMtug66+/3uzevbu5b9++Rr2vvLzc7NWrl/m3v/3NR5VZ6+DBg2Z0dLT57LPP+ruUozrttNPMs846q1Hvac3fC0nme++9V/P5jz/+aEoy9+/fX+u6Cy+80LzoootauLoj/bLew5WXl5vTp083R4wYYebl5TVq3Nby7+9oX1+1HTt2mJLMBQsWtExRR3Gsevv162fOmTOn0eO2lu8HAADV2tyMxpw5c/Txxx9r4cKF6tKlS6Pe63Q6NWLECG3fvt1H1VkrNjZWffv2bdX17tmzRwsWLNA111zTqPcF0veieo38gQMHaj1/4MCBVr1+vqKiQhdddJH27Nmjr7766qizGXUJhH9/1Xr27KkOHTq0+loXLVqkrVu3Nvq/Fymwvh8AgODQZoKGaZqaM2eO3nvvPX3zzTdKTk5u9Bhut1vr169Xp06dfFCh9QoLC7Vjx45WXe+8efPUsWNHnXnmmY16XyB9L5KTk5WYmKivv/665rn8/HwtXbpUxx13nB8rq191yNi2bZsWLFig9u3bN3qMQPj3Vy0lJUXZ2dmtvtbnnntOo0aN0rBhwxr93kD6fgAAgkObCRo33nijXn75Zb366quKiopSenq60tPTVVJSUnPNlVdeqb/85S81n8+dO1dffvmldu7cqVWrVunyyy/Xnj17mvTbxJbwhz/8Qd999512796txYsX69xzz5XdbteMGTP8XVqdPB6P5s2bp6uuukoOR+0NzgLte1FYWKg1a9ZozZo1krwN4GvWrNHevXtlGIZuuukm3X333frwww+1fv16XXnllercubPOOeecVldvRUWFLrjgAq1YsUKvvPKK3G53zX8v5eXlNWOcfPLJevLJJ2s+b03//o729RUWFurWW2/VkiVLtHv3bn399dc6++yz1bt3b02dOrXFaz1WvdXy8/P11ltv1ftvvjV/PwAAqJO/125ZRVKdf+bNm1dzzaRJk8yrrrqq5vObbrrJ7NatmxkSEmImJCSYZ5xxhrlq1aqWL76BLr74YrNTp05mSEiImZSUZF588cXm9u3b/V1Wvb744gtTkrl169YjXgu078XChQvr/PdV/TV4PB7z9ttvNxMSEkyXy2WefPLJdX7draHeXbt21fvfy8KFC2vG6N69u3nnnXfWfN6a/v0d7esrLi42TzvtNDM+Pt50Op1m9+7dzWuvvdZMT0/3S63Hqrfa008/bYaFhZm5ubl1jtGavx8AANTFME3T9HmaAQAAABBU2szSKQAAAACtB0EDAAAAgOUIGgAAAAAsR9AAAAAAYDmCBgAAAADLETQAAAAAWI6gAQAAAMByBA0AAAAAliNoAAAAALAcQQNt3vz58zVw4ECFh4drwIAB+uSTT/xdEgAAQJtH0ECb9s4772jOnDm6/fbbtWHDBk2dOlXXX3+9v8sCAABo8wzTNE1/FwH4yvHHH69TTjlFf//73yVJX331lS688ELl5ub6tzAAAIA2jhkNtFkFBQVasmSJzjjjjJrnvvjiC40YMcKPVQEAAAQHh78LAHxl7dq1stlsGjZsmIqLi/Xqq6/q8ccf13vvvefv0gAAANo8ggbarDVr1qh///5auXKlTjjhBEnSeeedp2nTpvm5MgAAgLaPpVNos9asWaORI0dqyJAhWrp0qR5++GF9/vnnmjt3rr9LAwAAaPOY0UCbtWbNGl1xxRWKjo7W2LFjNXbsWG3dulVLly71d2kAAABtHjMaaJMqKyu1ceNGDRgwoNbza9eurVlGBQAAAN9hRgNt0pYtW1RaWqq5c+cqPj5e4eHheuqpp7R7927Nnj3b3+UBAAC0eQQNtElr1qxRp06dFBYWpokTJyoiIkInnHCCFi5cqMTERH+XBwAA0OYRNNAmrVmzRuPGjWMrWwAAAD+hRwNt0po1azR06FB/lwEAABC0CBpok9auXUvQAAAA8CPDNE3T30UAAAAAaFuY0QAAAABgOYIGAAAAAMsRNAAAAABYjqABAAAAwHIEDQAAAACWI2gAAAAAsBxBAwAAAIDlCBoAAAAALEfQAAAAAGA5ggYAAAAAyxE0AAAAAFiOoAEAAADAcgQNAAAAAJYjaAAAAACwHEEDAAAAgOUIGgAAAAAsR9AAAAAAYDmCBgAAAADLETQAAAAAWI6gAQAAAMByBA0AAAAAliNoAAAAALAcQQMAAACA5QgaAAAAACxH0AAAAABgOYIGAAAAAMsRNAAAAABYjqABAAAAwHIEDQAAAACWI2gAAAAAsBxBAwAAAIDlCBoAAAAALEfQAAAAAGA5h78LAAAAQGAxTVPFxcWSpPDwcBmG4eeK0BoxowEAQBC6+uqr1aNHD3+XgQBVXFysyMhIRUZG1gQO4JcIGgAABKD58+fLMIyaP6Ghoerbt6/mzJmjAwcO+Ls8AGDpFAAAgWzu3LlKTk5WaWmpfvjhBz311FP69NNPtWHDBoWHh9f7vmeeeUYej6cFKwUQbAgaAAAEsGnTpmn06NGSpGuuuUbt27fXww8/rA8++EAzZsw44vqioiJFRETI6XRaVoPH41F5eblCQ0MtGxNA4GPpFAAAbchJJ50kSdq1a5euvvpqRUZGaseOHTrjjDMUFRWlyy67TFLdPRpFRUW65ZZb1LVrV7lcLvXr10//+te/ZJpmresMw9CcOXP0yiuvaNCgQXK5XPr8889b5OsDEDiY0QAAoA3ZsWOHJKl9+/aSpMrKSk2dOlUnnHCC/vWvf9W7nMo0Tf3qV7/SwoULNXv2bA0fPlxffPGFbr31VqWmpuqRRx6pdf0333yjN998U3PmzFGHDh1oLAdwBIIGACComKapkgq3v8uoEea0N2tr0Ly8PGVlZam0tFQ//vij5s6dq7CwMJ111ln66aefVFZWpgsvvFD33nvvUcf58MMP9c033+juu+/WbbfdJkm68cYbdeGFF+qxxx7TnDlz1KtXr5rrt27dqvXr12vgwIFNrh1A20bQAAAElZIKtwbe8YW/y6ixae5UhYc0/f8cn3LKKbU+7969u1555RUlJSXVPHfDDTccc5xPP/1Udrtdv/vd72o9f8stt+jtt9/WZ599pjlz5tQ8P2nSJEIGgKMiaAAAEMD+/e9/q2/fvnI4HEpISFC/fv1ksx1qwXQ4HOrSpcsxx9mzZ486d+6sqKioWs8PGDCg5vXDJScnW1A9gLaMoAEACCphTrs2zZ3q7zJqhDntzXr/2LFja3adqovL5aoVPKwSFhZm+ZgA2haCBgAgqBiG0aylSm1V9+7dtWDBAhUUFNSa1diyZUvN6wDQGGxvCwAAdMYZZ8jtduvJJ5+s9fwjjzwiwzA0bdo0P1UGIFDxKx0AAKDp06drypQpuu2227R7924NGzZMX375pT744APddNNNtXacAoCGYEYDAADIZrPpww8/1E033aSPP/5YN910kzZt2qQHH3xQDz/8sL/LAxCADPOXx30CAAAAR1FUVKTIyEhJUmFhoSIiIhr4xiwpP1UybN4/NofULllyhPiwWvgLS6cAAADgG2UF0oZ3pb1LpH1LpZwdR17jipH6nib1P0vqfYrkimz5OuETzGgAAACgUY45o1FWIC17Rlr8hFSSU/u1yATvR9MjVZRI5YWHXguJkn71mDT4fB9Wj5bCjAYAAACsYZrS0qel7+4/FDDiekmDz5O6jpO6jJbC2h263uOWUlZIWz6SNn8kHdwtvT1L2r9GOvlOyc6PqoGMGQ0AAAA0Sp0zGpXl0sc3SWte8V4U10ua9Cfv7ERDAoPHLX09V/rxUe/nPSdLF8yTwuN88SWgBRA0AAAA0ChHBA17pfTGFdKu7yTDLk29RxpzTdNmJDa8K31wo1RRLCUOka75hmbxAMX2tgAAAGi6vP3S86d7Q4YzQrr0DWn89U1f9jT4POmaBVJ4eyl9vfT9g9bWixZD0AAAAEDTvX21lLFJikyUZn0m9Tm1+WMmDJLO+Jf38aKHvD0bCDgEDQAAADRd+nopLE6a/YXUaZh14w4+Txp4jmS6pfdvkCrLrBsbLYKgAQAAgGYwpPOfkdr1sH7oMx+Swjt4Z0y+e8D68eFTBA0AAAA0TsaWQ49PuNl70J4vRHSQznrY+/iHR6S0db65D3yCoAEAAICGqyiR3v31oc8n/p9v7zfwbO8f0y39+Jhv7wVLETQAAIAkadu2bTrttNMUExMjwzD0/vvv+7sktEaLn5Sytx363Gb3/T0n3uL9uOl9qSDd9/eDJQgaAAAEoPnz58swjJo/DodDSUlJuvrqq5WamtqkMa+66iqtX79e//znP/XSSy9p9OjRFleNgJe/X/rh4Za/b6dhUtfxkqdSWjGv5e+PJuFcdwAAAtjcuXOVnJys0tJSLVmyRPPnz9cPP/ygDRs2KDQ0tMHjlJSU6KefftJtt92mOXPm+LBiBLSv53oP0ksaLWlhy9573K+lfUuklfO8Mxwc4tfqMaMBAEAAmzZtmi6//HJdc801evbZZ/WHP/xBO3bs0IcfftiocTIzMyVJsbGxltVWWloqj8dj2Xjws5SV0trXvI9Pm9vy9x/wK+9ZHYUHpM2N+/cN/yBoAADQhkycOFGStGPHjprntmzZogsuuEBxcXEKDQ3V6NGjawWRu+66S927d5ck3XrrrTIMQz169Kh5PTU1VbNmzVJCQoJcLpcGDRqk559/vtZ9v/32WxmGoddff11/+9vflJSUpPDwcOXn50uSli5dqtNPP10xMTEKDw/XpEmT9OOPP9Ya46677pJhGNq+fbuuvvpqxcbGKiYmRjNnzlRxcfERX+vLL7+ssWPHKjw8XO3atdOJJ56oL7/8stY1n332mSZOnKiIiAhFRUXpzDPP1MaNG5vwNxvkTFP6/M/ex8NmSJ1HtHwNdqc0epb38dKnW/7+aDSWTgEA0Ibs3r1bktSuXTtJ0saNG3X88ccrKSlJf/7znxUREaE333xT55xzjt555x2de+65Ou+88xQbG6ubb75ZM2bM0BlnnKHIyEhJ0oEDBzR+/HgZhqE5c+YoPj5en332mWbPnq38/HzddNNNte7/j3/8QyEhIfrDH/6gsrIyhYSE6JtvvtG0adM0atQo3XnnnbLZbJo3b55OOukkLVq0SGPHjq01xkUXXaTk5GTde++9WrVqlZ599ll17NhR999/f801f//733XXXXdpwoQJmjt3rkJCQrR06VJ98803Ou200yRJL730kq666ipNnTpV999/v4qLi/XUU0/phBNO0OrVq2uFKRzD+rellGWSM0I6+U7/1THqaun7B7217F/tn8CDhjMBAAgmHo9plhW2nj8eT5O+jHnz5pmSzAULFpiZmZnmvn37zLffftuMj483XS6XuW/fPtM0TfPkk082hwwZYpaWlh72V+AxJ0yYYPbp06fmuV27dpmSzAcffLDWfWbPnm126tTJzMrKqvX8JZdcYsbExJjFxcWmaZrmwoULTUlmz549a56rvlefPn3MqVOnmp7Dvtbi4mIzOTnZPPXUU2ueu/POO01J5qxZs2rd69xzzzXbt29f8/m2bdtMm81mnnvuuabb7a51bfU9CgoKzNjYWPPaa6+t9Xp6eroZExNzxPM4isoK03x0mGneGW2a3z1gmqZpFhYWmpJMSWZhYWHL1vP2Nd5a3r2+Ze+LRmNGAwAQXCqKpXs6+7uKQ/66XwqJaPLbTzml9kFpPXr00Msvv6wuXbooJydH33zzjebOnauCggIVFBTUXDd16lTdeeedSk1NVVJSUp1jm6apd955RxdddJFM01RWVlat97/++utatWqVjj/++Jrnr7rqKoWFhdV8vmbNGm3btk1/+9vflJ2dXWv8k08+WS+99JI8Ho9stkOrua+//vpa102cOFHvvfee8vPzFR0drffff18ej0d33HFHrfdJkmEYkqSvvvpKubm5mjFjRq267Xa7xo0bp4ULW7iROZBtel86uEsKi5PG3eDvaqRx10nr35Q2vCOd8aDkivR3RagHQQMAgAD273//W3379lVeXp6ef/55ff/993K5XJKk7du3yzRN3X777br99tvrfH9GRka9QSMzM1O5ubn63//+p//973/1vv9wycnJtT7fts173sJVV11V79eQl5dXs9RLkrp161br9erXDh48qOjoaO3YsUM2m00DBw6sd8zq+5500kl1vh4dHV3ve3EYj0da9JD38fgbWscP9UmjpHY9pIO7pR1few/zQ6tE0AAABBdnuHcWobVwhjfr7WPHjq057+Kcc87RCSecoEsvvVRbt26t2fHpD3/4g6ZOnVrn+3v37l3v2NXvv/zyy+sNCkOHDq31+eGzGYeP8eCDD2r48OF1jlHdD1LNbq/7ADjTNOut9Zeq7/vSSy8pMTHxiNcdDn4EapCfP5cyNkkhUdLYa/1djZdhSP3OlJb8W9ryKUGjFeO/MgBAcDGMZi1Vas3sdrvuvfdeTZkyRU8++aRmzfLu0ON0Oo9YYtUQ8fHxioqKktvtbtL7JalXr16SvDMITR2jrjE9Ho82bdpUb3ipvm/Hjh0tu2/QMU1p0b+8j8deI4W1O/r1Lan/Gd6gse0LyV0p2fmRtjVie1sAANqQyZMna+zYsXr00UcVHR2tyZMn6+mnn1ZaWtoR11afnVEfu92u888/X++88442bNjQ6PdL0qhRo9SrVy/961//UmFhYZPG+KVzzjlHNptNc+fOPeKcjupZj6lTpyo6Olr33HOPKioqLLlvoHB73MouydaBogNKLUxVRnHGsd9Ul13fSakrJUeoNP431hbZXF3He4NPyUFp70/+rgb1IP4BANDG3Hrrrbrwwgs1f/58/fvf/9YJJ5ygIUOG6Nprr1XPnj114MAB/fTTT0pJSdHatWuPOtZ9992nhQsXaty4cbr22ms1cOBA5eTkaNWqVVqwYIFycnKO+n6bzaZnn31W06ZN06BBgzRz5kwlJSUpNTVVCxcuVHR0tD766KNGfX29e/fWbbfdpn/84x+aOHGizjvvPLlcLi1fvlydO3fWvffeq+joaD311FO64oorNHLkSF1yySWKj4/X3r179cknn+j444/Xk08+2aj7BoIduTv0u29+p70Fe2s9f3qP0zX3+LkKc4TV8846fF81mzHyKimyo4VVWsDukPqe7j1AcOunUvJEf1eEOhA0AABoY84777yaWYRrr71WK1as0N///nfNnz9f2dnZ6tixo0aMGKE77rjjmGMlJCRo2bJlmjt3rt5991395z//Ufv27TVo0KBa51oczeTJk/XTTz/pH//4h5588kkVFhYqMTFR48aN03XXXdekr3Hu3LlKTk7WE088odtuu03h4eEaOnSorrjiipprLr30UnXu3Fn33XefHnzwQZWVlSkpKUkTJ07UzJkzm3Tf1mx1xmrN+XqO8su9hyQ6DIccNofK3GX6fPfn2luwV49PeVwJEQnHHixlpbR7kWRzSMf/zseVN1H/M71BY8sn0tR7vMsi0aoYZmM6qwAAANDqfLP3G/3x+z+qzF2mofFD9eRJT6pdqLenYkX6Ct387c3KLctVfFi8njjpCQ3qMOjoA749W9rwtjTsUuncp454uaioqKaJv7CwUBERfuh7Ki+SHugpVZZKNyyWEo7xNaHF0aMBAAAQwL7Y/YVu/vZmlbnLNKnLJD172rM1IUOSRieO1qtnvqpeMb2UWZKpmV/M1M68nfUPmJ/mPTtDksZfX/91/hYSIfWc7H285VO/loK6ETQAAAACVG5pru5ecrc8pkfn9D5Hj055tM4+jK5RXfXyGS9rVMIolVSW6G8//E2Vnsq6B13xnOSplLpNkDoN8/FX0Ez9zvB+3PqJf+tAnQgaAAAAAerRVY8qtyxXvWN7647j7pDDVn/7bWRIpO6beJ+inFFan7Ve8zbMO/KiilJpRdXz45rWP9Oi+k2TZEj7V0t5qf6uBr9A0AAAAAhAazPX6p1t70iS/jb+b3LanMd8T2JEov487s+SpP+s/Y+25mytfcGGt6XiLCm6i9T/LMtrtlxkR6nLGO/jrSyfam0IGgAAAAGm0lOpfy75pyTpV71+pVEJoxr83uk9p2tK1ymq9FTqrz/8VRXuqnNGTFNa+l/v47HXBM4heH2rTr3f9Z1/68ARCBoAAAAB5o2tb2hzzmZFhUTp/0b9X6PeaxiG7jjuDsW6YvXzwZ/1v/X/876wZ7GUvl5yhHnPzggUySd6P+7+UfrFAY7wL4IGAABAADlYelBPrvYeNnjTyJvUPqx9o8foENZBt42/TZL0/Prntb9w/6HZjKEXSeFxltXrc51HSM5wqSRHytzi72pwGIIGAABAAHl1y6sqrChU/7j+Or/P+U0eZ2r3qRqTOEblnnI9uvRe78F3UmA0gR/O7pS6jvM+3v2Df2tBLQQNAACAAFFcUazXtrwmSbpmyDWy2+xNHsswDN06+lYZMvRZyrdaG2L3bmkbiAff9TjB+3H3Iv/WgVoIGgAAAAHive3vKa8sT10iu+iUbqc0e7wB7Qfo7F7TJUkPxrWTOXpWs8f0ix4TvR/30KfRmhA0AAAAAkCFp0IvbnxRknT1oKubNZtxuN9GDlCYx6O1oS59EXHkYX8BobpPozibPo1WhKABAAAQAL7c/aX2F+1XXGiczu59tmXjdlz7pmbm5UuSHln9hMrcZZaN3WIcIYf6NPb86N9aUIOgAQAA0MqZpllzkvel/S9VqCPUmoGztku7vtNVeYXqGNpe+4v266VNL1kzdkujT6PVIWgAABCA5s+fL8MwZBiGfvjhyJ12TNNU165dZRiGzjrr0AnP1e956KGH6h1zxYoVNc/dddddMgxDWVlZ9dby7bff1oxb15/XX3+9mV8tftr/k7Ye3KowR5gu6X+JdQOveF6SFN7nNN085g+SpP+t+58OFB2w7h4tpSZo/OA9fBB+FyBHPgIAgLqEhobq1Vdf1QknnFDr+e+++04pKSlyuVx1vu/BBx/UDTfcoPDwcMtq+d3vfqcxY8Yc8fxxxx1n2T2C1QubXpAknd/nfMW4YqwZtKJEWvOK9/Ho2Toz+VS9vuV1rc1cq8dWPaZ7Jt5jzX1aSueR3sMGq/s0Og7wd0VBjxkNAAAC2BlnnKG33npLlZWVtZ5/9dVXNWrUKCUmJh7xnuHDh+vAgQP673//a2ktEydO1OWXX37En+7du1t6n2Czr2CfFu9fLEm6dMCl1g288T2pNFeK7Sb1PlmGYejPY/8sSfpo50dam7nWunu1BEeI1I3zNFoTggYAAAFsxowZys7O1ldffVXzXHl5ud5++21demndP5Qef/zxOumkk/TAAw+opKSkpUpFE7277V1J0oTOE9Q1qqt1A6+c7/046mqpagerwR0G65ze50iS7lt6nzxmgG0Ve/jyKfgdQQMAgADWo0cPHXfccXrttddqnvvss8+Ul5enSy6pfy3/XXfdpQMHDuipp56yrJaCggJlZWUd8cdkvXyTVXgq9N629yRJF/a90LqBD2yS9i2VbA5p+OW1Xvr9yN8r3BGuDdkb9NGOj6y7Z0uoPk+DPo1WgR4NAEBQMU1TJZWt57f4YY4wGYbRrDEuvfRS/eUvf1FJSYnCwsL0yiuvaNKkSercuXO975k4caKmTJlS06sRFtb88xNmzar7sLe0tLQ6l3Dh2L7d962yS7PVPrS9JnWdZN3A1bMZ/c6QohJqvdQhrIOuG3adHln5iP614l86Pul4dQjrYN29fanzSMkRKhVnSdnbpQ59/F1RUCNoAACCSkllica9Os7fZdRYeulShTub15B90UUX6aabbtLHH3+s008/XR9//LEef/zxY77vrrvu0qRJk/Tf//5XN998c7NqkKQ77rhDEydOPOL5uLi4Zo8drN7++W1J0rl9zpXT5rRm0PJiaV3VTmCjrq7zkisGXKFPd36qrQe36vYfb9d/Tv5PswNxi3CEeA/v2/uTlLKcoOFnBA0AAAJcfHy8TjnlFL366qsqLi6W2+3WBRdccMz3nXjiiZoyZYoeeOABXX/99c2uY8iQITrllFOaPQ68UgpSaprAz+tznnUDb/pAKs3zNoH3nFLnJU67U/dNvE8Xf3yxfkj9QW9sfcPabXV9qcvoQ0FjuIXN82g0ggYAIKiEOcK09NKl/i6jRpij+UuWJO/yqWuvvVbp6emaNm2aYmNjG/S+O++8U5MnT9bTTz/d4PegZfi8CXzkVZKt/nbd3u166+ZRN+v+5ffroRUPaWynseoZ09O6OnylS9UWyynL/VsHaAYHAAQXwzAU7gxvNX+sWo5y7rnnymazacmSJfXuNlWXSZMmafLkybr//vvZgaoVqfBU6L3t3ibwC/oee3aqwTI2S/uWSIZdGnH5MS+/dMClGt9pvErdpfrLor+owl1hXS2+0mWs9+OBjVJ5kX9rCXIEDQAA2oDIyEg99dRTuuuuuzR9+vRGvfeuu+5Senq6/ve///moOjTWopRFyirJUvvQ9prcdbJ1A6/0HvynftOkqGM36NsMm+4+/m5Fh0RrU/Ym/fWHv6rSU3nM9/lVdCcpuotkeqTUVf6uJqixdAoAgDbiqquuatL7Jk2apEmTJum7776r95qHH374iFPEbTab/vrXv9Z8vmjRIpWWlh7x3qFDh2ro0KFNqi1YfbzzY0nSWT3Psq4JvKJUWlu1DfLomQ1+W0JEgu6beJ9+t/B3+nz357IZNt024jZravKVLqOlTSne5VPJR25QgJZB0AAAALrrrrs0ZUrdjcGSdO+99x7xnN1urxU06tvp6s477yRoNEJeWZ6+3fetJGl6r8bNTh3Vlo+9J4FHd6m3Cbw+E7tM1EOTHtIt396iT3d9qsrSVj6r0WWMtOl9KWWFvysJaobJKToAAACtxls/v6W5P81Vn3Z99O6v3rVu4Bd+Je36Tpr0J2nKX499fR2+3vO1bvnuFlWUVmjTdZskSYWFhYqIiLCuTivsXSo9f5oU0VH6w89SIGzN2wbRowEAANCKfLzj0LIpyxzc4w0ZMqThlzV5mJO7n6wHTnxANuPQj5DvbntXHtNjQZEW6jRMsjmlogwpd6+/qwlaBA0AAIBWIqUgRasyVsmQoTOSz7Bu4DWveD/2nCS1696soU7rcZqeOfWZms/vW3afLv/0cr237T1lFmc2a2zLOEOlTlXL9djm1m/o0QAAAGglPtn5iSRpbKexSow49q5QDeJxS6urgsaIKywZckj8kJrH4Y5wrc9ar/VZ6yVJA+IGaGj8UEWHRCvGFaOokCi5TbfK3eUqd5fLbbrltDnltDkVYg9Rr9heGtx+sJx2i5req3UZI6Wu9AaNIRZuEYwGI2gAAAC0AqZp1tptyjI7F0r5KVJorNTfwnGrvDn9TX2a+ql+SPlBG7I3aHPOZm3O2dyoMULtoRoWP0zHdT5OM/rPULgz/NhvOpYuY6Sl/2VGw49oBgcAAGgFNmRt0IxPZijUHqqFFy1UZEikNQO/eZV3B6axv5bOeNCSIYuKihQZ6a3v8GbwrJIs/bT/J+0t2Ku8sjzlleWpoLxAdptdLrtLIbYQ2QybKs1KVbgrVFxZrI1ZG3Ww7GDN2EmRSbp9/O06Pun45hV5cLf0WFWvxl9SvMup0KKY0QAAAGgFPtrxkSRpStcp1oWMomxpi3c5llXLpo6mQ1iHRm/Ja5qmduTu0PIDyzVvwzylFqbq+gXXa3rP6bp1zK1qF9quacXEdpci4qWiTCl9ndR1bNPGQZPRDA4AAOBnbo9bX+z+QpJ0Vi8Llzetf1PyVEiJQw81R7cyhmGod7vemtF/ht4/+31dPuByGTL00c6PdMVnVyi3NLepA3uXT0nSvmWW1YuGI2gAAAD42eqM1couzVZUSJSO63ScdQNXnwQ+4nLrxvShcGe4/jT2T3r5jJfVKaKT9uTv0e8X/l5l7rKmDdhltPcjfRp+QdAAAADws6/2fCXJu2zKst2XDmyS0tZKNoc0OLB2XRoaP1RPnfKUopxRWpWxSrf/cHvTzupIGuX9uH+1tQWiQQgaAAAAfuQxPVqwd4Ek6bTup1k38LrXvR/7TJUi2ls3bgvpFdtLD095WA7Doc92f6YnVz/Z+EE6Dfd+zN0jFedYWh+OjaABAADgR+sy1ymjOEMRzggd19miZVMet7TuTe/jYZdYM6YfjO80XndOuFOS9Mz6Z/T1nq8bN0BYrBTXy/t4/ypri8MxETQAAAD8aMEe72zGpC6TFGIPsWbQXd9JBWneszP6TrVmTD85p/c5mjlopiTpgeUPqLSytHEDdB7h/ZjK8qmWRtAAAADwE9M0a/ozTu1+qnUDr33D+3Hw+ZLDZd24fnLD8BuUEJ6g/UX7NX/j/Ma9OWmk9yN9Gi2OoAEAQACrrKzUP//5TyUnJys8PFyTJk3Szz//7O+y0ECbcjZpf9F+hTnCmn9AXbWyQmnzh97Hw2ZYM6afhTnCdMvoWyRJz61/TulF6Q1/c/WMBkunWhxBAwCAAOV2u3XeeefpkUce0TXXXKN//vOf2rRpk6ZPn67Kykp/l4cG+Gq3dzbjhKQTFOYIs2bQzR9JFcXe3oTq7V3bgNN7nK6RHUeq1F2qR1Y+0vA3dhomGTbvUrL8NN8ViCMQNAAACFD/+te/9PXXX+u7777TbbfdpptvvlmPPfaYfv75Z3377bf+Lg/HYJqmb3abqj47Y9gM76F1bYRhGPrT2D/JkKFPd32q1RkNXAoVEiHF9/c+ZvlUiyJoAAAQgPLy8nTPPffopptu0qBBg2qenzBhgiRp7dq1/ioNDbQtd5v25O+Ry+7SxC4TrRk0f7+063vv46EXWTNmKzKw/UCd1+c8SdL9y+6XaZoNe2PN8imCRksiaAAAEIBeeeUVFRQU6Ne//nWt551O72FvBQUF/igLjbBw70JJ0nGdj1OEM8KaQTe+L8mUuh0ntetuzZitzG9H/FZhjjBtzN6oFQdWNOxN9Gn4BUEDAIAA9O6772rgwIGKiIhQVlZWzZ99+/ZJkiIiLPrBFT6zKHWRJGlyl8nWDbrhHe/HwedbN2Yr0z6svX7V61eSpJc2vdSwN3Wu2nkqdZXU0FkQNJvD3wUAANCSTNNUcXGxv8uoER4eLqOR6+jdbreWLFmioqIixcfH13lNcnKyFeXBRw6WHtS6zHWSvI3glsjZJaWu8DY+DzzbmjFbqcsGXKY3tr6hb/d9q335+9Q1uuvR35A4WLI5pZIcKXdvm53taW0IGgCAoFJcXKzIyEh/l1GjsLCw0bMPO3bsUFFRkf74xz/q1FNrn73w/PPP67XXXtPQoUOtLBMWW7x/sUyZ6tuurxIiEqwZdOO73o/JJ0qRHa0Zs5VKjknWxKSJWpS6SK9ueVV/Gvuno7/B4ZISBkppa73LpwgaLYKlUwAABJjdu3dLkiZPnqxTTjml1p+MjAwlJCSob9++/i0SR1W9bGpikkVN4JK0oSpotOFlU4e7fODlkqT3tr+nwvLCY7+hMwf3tTRmNAAAQSU8PFyFhQ34oaSFhIeHN/o9RUVFko7sw8jLy9OiRYs0a9YsS2qDb7g9bv2Y+qMkWbfbVMYW6cAG7/Kg/mdZM2Yrd1yn49Qrppd25O3Qe9vf0xUDrzj6G5JGSivnefs00CIIGgCAoGIYRsA3SkdFRUnSEYHphRdeUHl5uW644Yaa5yorK/X3v/9dzz33nMrLy3XllVfqoYceanRfCKyzIXuDcstyFeWM0rD4YdYMWr1sqvfJUnicNWO2coZh6LKBl2nuT3P1yuZXdGn/S2W32et/Q/XOU2lrJY9HsrGwx9f4GwYAIMAMHTpUNptNCxcurHkuJSVF//jHP3TllVfW6s+45ZZbtHHjRm3cuFHbtm3TggUL9NZbb/mjbFT5IfUHSdKEpAly2Cz4na9pBsVuU3U5q+dZinHFKLUwVd+mfHv0i+MHSI5QqSxfytnRIvUFO4IGAAABpmPHjjrnnHP02GOP6c4779TDDz+sCRMmKCkpSU888UTNdSkpKXrxxRc1b948tWvXTu3atdO0adO0cuVKP1aPRSne/gzLdptKXydlb/f+EN1vmjVjBogwR5jO7+MNV+9ve//oF9sdUmJVCKdPo0UQNAAACEDPPvuspk+froceekgPPPCAzjnnHC1atEjR0dE113z//fcaN26cYmJiap7LyclRQoJFuxyh0bJKsrQxe6MkC4NG9WxG36mSK8qaMQPI2b28W/n+kPqDDpYePPrFnYd7P+5f49Oa4EWPBgAAAahdu3Z65513jnpNdna2YmNjaz6vqKjQF198oZkzZ/q4OtSnugl8YPuB6hDWofkDmqa0+SPv40HnNn+8ANQztqcGxA3Q5pzN+nL3l7q4/8X1X9xpuPdj2pqWKC3oMaMBAEAbNWrUKH3//fdKTU1Vbm6urrvuOg0fPlwTJkzwd2lBy/JtbTM2STk7JbtL6n3qsa9vo87seaYk6ZNdnxz9wl82hMOnCBoAALRREyZM0PXXX68RI0aoV69eCgkJ0WuvvebvsoKW2+PW4v2LJVm4bKp6NqP3yZKr9RxE2dKmJU+TIUOrM1YrpSCl/gs79JUcYVJ5obevBT5F0AAAoA274447lJGRoezsbP33v/8N+K19A9mWnC0qKC9QpDNSgzsMtmbQ6qAxYLo14wWojuEdNbbTWEnSp7s+rf9Cu0PqVNUQzvIpnyNoAAAAtIBl6cskSaMSRlmzrW32Du8hfYZd6nt688cLcGcmVy2f2vmJTNOs/8LqPg12nvI5ggYAAEALqA4aYxLHWDPglo+9H5MnBs0hfUdzSvdT5LK7tDNvp7bkbKn/QnaeajEEDQAAAB+r9FRq1YFVkqSxiWOtGZRlU7VEhURpUpdJkryzGvWq1RDuboHKghdBAwAAwMc2ZW9ScWWxokOi1S+uX/MHzN8vpSyXZEj9z2r+eG1E9e5Tn+76VO76QkSHvpIzXKoooiHcxwgaAAAAPla9bGp0wmjZDAt+/NpS9Rv7rmOlqMTmj9dGTEyaqKiQKGWWZGpd1rq6L7LZDzshfE2L1RaMCBoAAAA+tjx9uSTV7IzUbJs/9H5k2VQtTruz5oyShfsW1n9hTZ8GDeG+RNAAAADwoQp3hVZneH+gtaQRvDhH2u09YZxlU0ea0m2KJOnbfd/WfxEnhLcIggYAAIAPbcjeoJLKErVztVPv2N7NH3Dbl5LplhIGS3HJzR+vjTm+8/Fy2BzalbdLu/N2130RDeEtgqABAADgQ8vSqvozEi3qz/j5c+9Hzs6oU1RIlMYkeGeO6p3V6NBHckZIFcVS1rYWqy3YEDQAAAB8qKY/w4ptbSvLpe1fex/3m9b88dqo6uVT9fZp2OycEN4CCBoAAAA+Uu4u15rMNZIsChp7F0tl+VJEvNR5ZPPHa6Mmd5ksSVqTuUY5pTl1X8QJ4T5H0AAAAPCRtZlrVeYuU4ewDkqOsaCf4ucvvB/7TJVs/BhXn06RnTQgboA8pkffp3xf90WcEO5z/AsFAADwkRXpKyRJYxLGyDCM5g1mmtLWz7yP+9GfcSyTu06WdJQ+jeqG8PR1NIT7CEEDAADAR6q3tR2ZYMEyp6yfpYO7JHuI1HNK88dr46Z09f4dLd6/WKWVpUde0L73YQ3hP7dwdcGBoAEAAOADbo+75nTq4R2HN3/A6tmMHhMlV2Tzx2vj+sf1V2JEokoqS2pOZq/l8IZwlk/5BEEDAADAB7bnbldRRZHCHeHqE9un+QNW92ew21SDGIZR0xRe7+5TNedprGmRmoINQQMAAMAH1maulSQNjR8qu83evMGKc6R9S7yP+05tZmXB48QuJ0qSFqculmmaR17AzlM+RdAAAADwger+DEuWTW37SjI9UsdBUmy35o8XJEYljJLT5tT+ov3aW7D3yAuqd55KX09DuA8QNAAAAHxgTcYaSdLw+OHNH6z6NHB2m2qUcGe4RnT0Lo9avH/xkRe07y2FRNIQ7iMEDQAAAItllWQppTBFhgwNjR/avMHcldKOb7yP+7BsqrGO63ycpHqChs0uJVY3hLN8ymoEDQAAAIutzfD2Z/Ru11tRIVHNGyx1pVSaK4XGSl1GN7u2YDOh8wRJ0rK0ZarwVBx5AQf3+QxBAwAAwGJrMtdIsmjZ1PYF3o+9TvL+Bh6N0j+uv9q52qm4sljrMtcdeQE7T/kMQQMAAMBiljaCb//K+7H3Kc0fKwjZDJvGdx4vqZ7lU9U7T6Wt8y5Tg2UIGgAAABYqc5dpU/YmSRbMaBRmHuod6H1y88YKYtXLp5bsX3Lki9UN4ZUlNIRbjKABAABgoc3Zm1XhqVBcaJy6RnVt3mA7qw6aSxwiRSU2v7ggdVwnb0P4huwNyivLq/2izSZ1GuZ9zPIpSxE0AAAALHT4traGYTRvsG3Vy6ZObd44QS4hIkG9YnrJY3q0NG3pkRdwcJ9PEDQAAAAsVNMI3tz+DI9H2vG19zH9Gc121G1u2XnKJwgaAAAAFjFN07pG8LTVUnG25IqWuo5tfnFBrrpP46f9P8k0zdovVu88lb6ehnALETQAAAAsklqYqpzSHDlsDg1sP7B5g22vms3oOUmyO5tfXJAblTBKTptT+4v2a0/+ntovxvWSQqKqGsK3+qfANoigAQAAYJENWRskSf3b9ZfL7mreYPRnWCrcGa5h8d6m7+UHltd+0WaTOnFCuNUIGgAAABZZn7VekjS4w+DmDVScI6Wu8D6mP8MyYxLHSJKWpy8/8sXq5VP0aViGoAEAAGCR6hmNIfFDmjfQzm8l0yPFD5BikppfGCQdChor0lfU36fBFreWIWgAAABYoNJTWXNQX7NnNHZ84/3IIX2WGho/VCG2EGWWZB7Zp1G9xW36esld0eK1tUUEDQAAAAvsyN2hUnepIp2R6hHdo+kDmaa0o+qgvl4nWVIbvFx2l4bGe3sxjujTiOvp3eGrslTK3OKH6toeggYAAIAFqvszBnUYJJvRjB+xsrZJ+SmS3SV1n2BRdahWb5/G4SeE06dhCYIGAACABWr6Mzo0sz+jetlU9wmSM6yZVeGXjt6nMdz7kZ2nLEHQAAAAsIBlO05VBw2WTfnEUfs0anaeImhYgaABAADQTMUVxdqeu11SM2c0Ksuk3Yu8jwkaPnHUPo3qoHFgo1RZ3sKVtT0EDQAAgGbanLNZHtOjjuEd1TG8Y9MH2rdMqiiWIjpKCYOsKxC11Nun0S5ZCo2R3GVS5mY/VNa2EDQAAACayfL+jF5TJMNoZlWoT719GoZxaJtblk81G0EDAACgmejPCCxDOgyR0+Y8Rp/Gmhavq60haAAAADSTJTMaRVlS2lrv456Tm18U6hXqCD1Kn8Zw70dmNJqNoAEAANAM2SXZSi1MlSFDA9sPbPpAO7+VZEoJg6WoRKvKQz3q7dOo1RBe1sJVtS0EDQAAgGbYmL1RkpQck6yokKimD3R4fwZ8bnTCaEnSygMra/dpxHaXwtpJngopY5OfqmsbCBoAAADNYEl/hmlKOxZ6H/ckaLSEIR2GyGE4lFGcobSitEMv0BBuGYIGAABAM1gSNDK3SgX7JUeo90Rw+Fy4M1wD2g+QJK3KWFX7RQ7uswRBAwAAoIlM09SmLO/ymsHtmxE0qpdNdTtOcoZZUBkaYkRHb6BYfeAXgYKgYQmCBgAAQBOlFaXpYNlBOWwO9Yvr1/SBdlYtm6I/o0WN7DhS0lFmNDI2SxWlLVxV20HQAAAAaKLqbW37xPZRiD2kaYNUlkm7f/A+5vyMFjW843BJ0vbc7coryzv0QkwXKbyD5KmUDmzwT3FtAEEDAACgiap3nBrUYVDTB9m3TKooliLipY7NGAeN1j6svXpE95Akrc1ce+gFw2D5lAUIGgAAAE1UHTSa1Z9RvWyq52TJxo9mLa26T2PVARrCrca/ZgAAgCbwmJ6aRvBmzWhUb2vLsim/qGkIz/hFoEjy9m8o9RcBBA1G0AAAAGiCfQX7VFBRIJfdpV6xvZo2SHHOod+Y95xsWW1ouJEJ3kCxPmu9ytyHnQRefZZG1laprLDlC2sDCBoAAABNUN0I3i+un5w2Z9MG2fWdJFOKHyBFd7auODRYt6huiguNU4WnQpuyDzsJPLqTFNVJMj1S+nr/FRjACBoAAABNUNMI3r45y6aqzs9gW1u/MQzj0Da39fZpsHyqKQgaAAAATbAxq6oRvKkngpumtONb7+OeBA1/qrdPo3NVnwYN4U1C0AAAAGgkt8etzTmbJTVjRiNnp5S3V7I5pR7HW1gdGqu6T2N1xmp5TM+hF9h5qlkIGgAAAI20K2+XSipLFOYIqzmHodGql011Gy+FRFhWGxqvX1w/hTnClF+er525Ow+90Hm492P2dqk0r873on4EDQAAgEaq7s8Y2H6g7DZ70wbZ/rX3I/0Zfue0OTW0w1BJ0urMw2YvIjpIMd28j/evafnCAhxBAwAAoJGqd5xq8rKpynJp9yLv414nW1QVmmNYx2GSpLUZa2u/kMTyqaYiaAAAADRS9TaoTW4E37dUKi+UIuKlxKEWVoamGh4/XJK0NvMXQYM+jSYjaAAAADRChadCW3K2SGrGjMb2Bd6PvU6SbL75cczjMZVXUqG0vBLll1bINE2f3KetGBrvDXy783frYOnBQy8QNJrM4e8CAAAAAsn2g9tV7ilXVEiUukZ1bdogO6r7M5q/bKqwrFLLd+do0/58bU7L15b0AqXnlaqwrLLWdXaboZgwpxKjQzWoc7QGJ8VocFK0hiTFKsTB755jXDHqGdNTO/N2am3mWk3uOtn7QvUJ4bl7pKJsKaK9v0oMOAQNAACARtiQfag/wzCMxg9QmHHopOleJzWphn05xfpq0wEt3JqhJTuzVeGuf7bCYTNU6THl9pjKKSpXTlG5NqXl662VKZKkKJdDk/rF69SBCZrcr6Niwpp4ynkbMLzjcO3M26k1GWsOBY2wWCmul5SzQ0pbLfU+xZ8lBhSCBgAAQCM0+6C+6m1tE4dKkfENfltphVufbUjTG8v3acnOnFqvdW8frmFdYjWgU7QGdIpSt7hwRYc5FRXqkMthV2mFW3klFTpYXK692cXasD9fG1PztDYlV1mF5fp4XZo+XpemELtNZwxJ1GXju2t093ZNC1IBbHj8cL277V2tyVxT+4XOI7xBI5Wg0RgEDQAAgEao3nFqcPsmBo3qbW17N2zZVGpuieb9sEtvrNinglLvcijDkMYlx+mUAQk6qX9H9YyPPOoYoU67Qp12JUSHqn9itE4blCjJ28exNiVXX246oC83pmtHZpHeX7Nf76/Zr74Jkbp6QrLOH5Ukl6OJW/gGmOqdpzZkbVCFp0JOW9XsTtJIacPb0v5Vfqwu8BA0AAAAGqikskTbc7dLkgZ1aEIjuMdzaEbjGP0ZG1Lz9Myinfp4XZrcHu/SqKTYMF00uqsuGN1FSbFhjb//L9hshkZ0a6cR3drpj1P7aV1Knl5Zukcfrt2vnw8U6q/vrdfjX2/TdZN66pIx3RQW0rYDR4/oHopxxSivLE9bc7YemrXq7D05XKkEjcYgaAAAADTQ1pytcptutQ9tr4TwhMYPkL5OKs6SQiKlruPqvGTV3oN64uttWrg1s+a543u31zUn9NSkvvGy2XyznMkwDA3rGqthXWN125kD9daKfXpm0U6l55fq7x9t0r8XbtecKb116bjuPrl/a2AzbBraYagWpS7S2sy1h4JGp6GSYZMK06X8/VJ0Z/8WGiAIGgAAAA1UfSL44A6Dm9a/UL3bVI+JkiOk1ksrdufosa+3adG2LEmSzZDOGtpZvz6xpwYnxTSr7saKCXPqmok9dcVx3fX2yhQ99e0OpRws0V0fbdLzP+7WnBObuNtWABjecbgWpS7Smow1umzAZd4nQyKkjgOlAxuk1JUEjQYiaAAAADRQzYngTVk2JUnbq5ZNHdafsWxXjh77+mf9uD1bkncb2vNGJOnGKb3Vo0NEs+ptLpfDrsvGdddFo7vqzRX79OiCbdqbU6w/vLn22G8OUNUH99XZEH5gg3f51IDpLV5XICJoAAAANFCzGsFL86R9SyRJZq+T9NOOLD35zXYt3uENGA6boQtGddFvJvdWt/bhltVsBafdpsvGdde5I5L03KJdemrBpprX/vT2Ot1+7gglRIf6sULrDO4wWHbDrvSidKUXpSsxwts4r6SR0uqXaAhvBIIGAABAAxSUF2h3/m5JTdzadsdCyVOpoqhkXf5Gulbv9Z4u7rQbumBUV/1mci91jWtdAeOXwkMc+u3JfTR9UHsl3+d97sO1+/XNjjzddEofzTw+WU57YB/+F+4MV992fbU5Z7PWZK7R6RGne1+obgjfv9rb1O+jE93bEv6GAAAAGmBTtve3+EmRSWoX2q5R7y0ur9SOxe9Kkl472F+r9+bK5bDpquO669tbp+je84a0+pBxuPgoV83j4V1jVFzu1j2fbtH0J37Qyj05R3lnYBgW793mdm3GYUvEEgZJdpd3Zipnp58qCywEDQAAgAaoXjY1sP3ABr9ne0aB/vnJJh13z1eKTlkoSVpsH6MbJvfSD386SX8/e7Al29T60yvXjNcDFwxVu3CntqQX6PynftKf31mnvOIKf5fWZMM7Dpckrc5YfehJu9O7+5TE8qkGYukUAABAAxy+49TRZBWW6eO1+/Xu6lStS8mTJA0ztivela9ye4Qe/b/rFR3h3yZvK9lshi4a3VWnDkjQfZ9t0Rsr9un15fu0YHOG5p49SNMGJwbcCePVQWNrzlaVVpYq1FHVf9J5pJSy3NsQPvQi/xUYIAgaAAAADVBfI7jHY2pzer4WbsnQ11sytGZfrkzv+Xpy2AxN7hev28IXSxulkL4nK6QNhYzDtYsI0f0XDNUFo7voz++s047MIv3mlVU6dWCC7j5ncEA1i3eO6KwOYR2UVZKljdkbNSphlPeFpOo+DWY0GoKgAQAAcAzZJdlKK0qTIUOJob20eHuWVu/L1YrdOVq1N1d5JbWXCQ3tEqPzRiRp+rDOah/pkp6+xftC39P9UH3LGtMjTp/8bqL+s3C7/vPtDn216YCW7szWndMH6byRSQExu2EYhobHD9eCvQu0NnPtYUGj6mPaOsld4V1OhXoRNAAAAH7B7TG1P7dEu7KKtCurSIvTfvC+UBGvE+9fcsT1oU6bTujdQSf1T9CU/vHqFHNY30VBupRW1VTc59QWqN7/Qp12/d9p/XTG0E7649vrtC4lT7e8tVafbUjTPecOUccAmN0YFj9MC/Yu0JqMNYeejOsluaKlsnwpY/Ohng3UiaABAACClmma2pdTok1pedq0P19bDxRoV1aRdmcXq7zSU3NdSIeVcsVL5UVJkqRuceEanBStUd3jNLp7Ow3sHF3/tq7bvvR+7DxSiuzo6y+pVemfGK13b5igp7/fqUcX/KwFmzO0fPf3uufcITpzaCd/l3dU1X0aazPXyjRN70yMzSZ1Hi7t+t67fIqgcVQEDQAAEDRM09T2jEL9uD1LP+7I1tKd2covrazz2hC7Td3bhyu5Q4T2OHO0v1yaOeZEzRk1VRGuRvwI9fMX3o9BsGyqLg67TTdO6a1TBiToD2+t1frUPN346ip9vSVJd/1qkKJDW+fyowHtB8hhcyinNEcpBSnqGt3V+0Lnkd6gkbpKGnW1X2ts7QgaAACgzdudVaR3V6fqvdUp2pdTUus1p91Q34QoDeocrf6J0eoZH6Fe8ZHqHBsmu82QaZqa9MYuSdLpfcY0LmRUlnkP6pOkvqdZ9eUEpH6JUXr3NxP0+Nfb9O+F2/XuqlQt3ZmjRy8ZrjE94vxd3hFcdpcGth+odZnrtCZzzaGgUd2nQUP4MRE0AABAm+T2mPpsQ5qe/2GXVu3NrXk+xGHTmB7tNKFXBx3fu4MGdopWiKP+o8VSClN0sOygnDanBsQNaFwRe36UKoqkyAQpcVgTv5K2w2m36ZbT+mlS33jd/OYa7csp0cVP/6SbT+mr30zpLbutdTWKD4sfpnWZ67Q2c62m95rufbJ656kDm6TyYikkcA5abGkEDQAA0Ka4PaY+XrdfT3yzXdszCiVJNkOa2Cde541M0mkDExUWYm/weOsz10uS+sf1V4g9pHHFbP3M+7HPqd71/ZAkje4Rp09/N1F3fLBR761O1UNf/ayfdmbr0YuHt6pG8WHxw/SSXtLazMNOCI9OkiITpcKqJv/ux/mvwFaOoAEAANqMn3Zk62/vr9eOzCJJUnSoQzOPT9Zl47o1+QfYdVnrJElDOgxp3Bs9Hmnzx97H/ac36d5tWVSoU49cPFzH9+6g29/foMU7snXG44v0+CUjNKF3B3+XJ8kbNCTp54M/q7iiWOHOcMkwpC6jpS0fS6krCBpHQbQGAAABL6eoXLe8uVYznlmiHZlFig136tap/fTDn0/Szaf2bdZvyatnNIbGN3KHof2rpIL9UkiU1HNyk+/f1l0wqos++u0J6p8YpazCcl3+3FI9/d0OmdWnHvpRYkSiEiMS5TE9Wp+1/tAL1X0aKcv9U1iAIGgAAICA9sm6NJ380Ld6Z1WKDEO6fHw3fXfrFN04pXezdzQqd5drc85mSdLQDo0MGps+8H7se5rkbD3LgVqj3h0j9d5vjtd5I5PkMaV7P9uiG15epYLSimO/2ceGxw+XpNrLp7qM9n5MWdnyBQUQggYAAAhIZZVu3f7+Bt346iodLK5Q/8QovX39BN19zhDFhFmzZerWnK2q8FSonaudukR1afgbTVPa/JH38QCWTTVEWIhdD104TP84Z7CcdkOfb0zXuf9ZrN1ZRX6tq3r5VK2D+zqPkAyblJ/iPZARdSJoAACAgLM3u1jnP7VYLy3ZI0n6zeRe+ui3J2hU93aW3qe6P2Nwh8HeA9sa6sBG6eAuyREq9Q6O08CtYBiGrhjfXW9cd5wSol3anlGoc/7zoxZvz/JbTdUH963LWndoOZcrSoqv2oEsZYV/CgsABA0AABBQftyepTOfWKQNqflqF+7UvJlj9MfT+9d/MnczrMv0Bo1G92ds/tD7sdfJkivS4qravpHd2unDOSdoWNdY5RZX6Irnl9WEypbWr10/uewu5ZXlaXf+7kMvdKnq00glaNSHoAEAAALGOytTdNXzy1RQWqkR3WL1ye8makq/jj67X3UDcKP7M1g21WwJ0aF649fjdc7wznJ7TN3+/gbd9eFGuT0t2yTutDs1qP0gSb9YPpVU3adB0KgPQQMAALR6pmnq8a+36Za31qrSY2r6sM567drx6hwb5rN7Hiw9qH0F+yRJg+MHN/yNWduljE2SzSH1O91H1QWHUKddj1w8XLdO7SdJmr94t254eaVKyt0tWkf18qk1mWsOPVndEL5/teRp2XoCBUEDAAC0ah6Pqb++t0EPf/WzJOn6Sb302MXDFeps+KF7TVE9m5Eck6zokOiGv7F62VTyiVKYtT0jwcgwDN04pbeemDFCIXabvtx0QDOeWaKswrIWq2FExxGSpNUZqw89Gd9fComUygulzC0tVksgIWgAAIBWy+0x9Ye31+q1ZXtlM6R/nDNYf57WXzZbIxqzm6i6P6PRB/WxbMonpg/rrFeuHafYcKfW7MvVef9ZrD3ZLbMjVfUWt7vydulg6UHvkza7d/cpieVT9SBoAACAVqnC7dFNb6zRu6tSZbcZevSSEbpifPcWu3+T+jMO7vEe1CdD6n+WbwoLYmN6xOmdGyaoW1y49uYU6/ynftKG1Dyf3zc2NFY9Y3pK+kWfRvXyKRrC60TQAAAArU55pUe/fXW1Plq7X067oX9fOlK/Gta5xe5/+EnQQ+IbMaOx4W3vx+SJUqTvmtSDWa/4SL19w3Ea2ClaWYVluuR/S7R4h++3v61ZPpV52PIpGsKPiqABAABalUq3Rze9sVqfb0xXiN2m/14+SqcPTmzRGnbn71ZBeYFC7aHq065Pw95kmtK6t7yPh1zku+KgjlGheuO68TquZ3sVllXq6ueX67P1aT69Z01DeF0zGhmbpbICn94/EBE0AABAq+HxmPrj2+v06XpvyPjflaN08oCEFq9jbcZaSdLA9gPltDXwlPEDG6XMzZI9hP6MFhAV6j1D5YwhiSp3e3Tjq6v01op9Prtf9YzGhqwNKnNXNaJHJUrRXSSZ3t2nUAtBAwAAtAqmaeq29zfo3dXenownLx2hyT48I+Noqrcxrf4tdoOsf9P7se9UKSzW6pJQh1CnXU/MGKlLxnSVx5RufXudnv9hl0/u1S2qm+JC41ThqdCm7E2HXqie1UhZ7pP7BjKCBgAA8DvTNHX3J5trdpd69OLhOm1Qyy6XOlz1NqbVv8U+Jo9HWv+O9zHLplqU3Wbo3vOG6JoTkiVJcz/epMcWbJNpWnuwn2EYdW9z22WM9+M+gsYvETQAAIDfPfnNdj1X9Zvo+88fqukt2Pj9S7mludqV562lelvTY9r7k5SfIrlipD6n+a441MkwDN125gD936l9JUmPLPhZ9322xfKwUWfQ6DrO+3HfUm+fDmoQNAAAgF+9tGSPHqo6jO/O6QN14eiufq2netlUckyyYkNjG/am6mVTA6dLzlCf1IWjMwxDvzu5j+44a6Ak6envd+qODzbK47Huh//DG8JrQkynYZLdJZXkSNk7LLtXW0DQAAAAfvPh2v2644MNkqTfndxHM49P9nNFh35bPbLjyIa9obJc2vi+9/GQC31TFBps1gnJuve8ITIMb4j94zvr5LYobAyMGyiX3aXcslztyq/qBXGESElV/1b2LbHkPm0FQQMAAPjFdz9n6v/eWCPTlK48rrtuPqWB28j6WHXQaHAj+PYFUmmuFJko9Zjos7rQcDPGdtPDFw2T3Wbo7ZUp+v3rq1Xh9jR7XKfdqcEdBkv6xTa3Xcd6P+5b2ux7tCUEDQAA0OJW7z2oG15eqUqPqenDOuuu6YNkGIa/y1K5u1wbszZKakQj+LrXvR8Hny/Z7D6qDI117ogu+velI+S0G/p4XZrmvLpK5ZXNDxt192mM937ct6zZ47clBA0AANCitmcUatb85Soud2tinw566MJhstn8HzIkaVP2JpV7yhUXGqduUd2O/YaibGnLp97Hw2f4tjg02umDO+npK0YpxGHTFxsP6PqXV6q0wt2sMesOGlUzGplbpOKcZo3flhA0AABAi0nLK9GVzy3VweIKDesaq/9e7v0hsLU4fFvbBs2wrH9T8lRInYZLiUN8Wxya5KT+CXr2ytEKddr0zZYMXfPCCpWUNz1sDIsfJkOG9uTvUVZJlvfJiA5S+97exykrLKi6bWg9/2UDAIA27WBRua58bpn255WqZ3yE5l09RhEuh7/LqmVVxipJDVw2ZZrSqpe8j0dc7sOq0Fwn9o3X/JljFR5i1w/bszRz/jIVlVU2aawYV4z6tvNuo7viwGGh4vBtbiGJoAEAAFpAUVmlZs5frm0ZhUqMDtWLs8YqLiLE32XVYpqm1masldTARvD9q6WMjZIjlN2mAsD4nu310uyxinQ5tGRnjq56fpkKSiuaNNboRO9p4CvSDw8aNIT/EkEDAAD4VHmlR9e/vFJr9uUqNtypl2aPVZd24f4u6wi783frYNlBuewuDYwbeOw3rK6azRgwXQqL9WltsMao7nF6+Zpxig51aMWeg7r8uWXKK2582BiT4D0NvHbQqGoIT10puZsWYNoaggYAAPAZt8fU/725Rou2ZSk8xK55V49Rn4Qof5dVp+rtSge1HySn3Xn0i8uLpfVvex+PuMK3hcFSw7vG6tVrxys23Km1+3J16bNLdLCovFFjjEzwnpuxI2+HskuyvU926CuFxkgVxdKBDVaXHZAIGgAAwCdM09TtH2zQx+vS5LQb+u/lozSiWzt/l1WvmoP6EhpwUN+Wj6WyfCm2G2dnBKDBSTF67drx/9/efcdHUSduHP/sbnpPSIFAIBBa6L3ZAEGx90MREBArep7lfnrWO/VO9Cx3nghWFBHh7IWmUpReUgAhECCE0JIQIIXUze78/lgIcEgJbHZSnvfrdZdlZnbyZF4Y8mS+3/nSKNCHjXsLue29leQdLj/r94f7hdM6zDX5OyknybXRaoVmR4ZPZWn4FKhoiIiISA0wDIMXZ6cxY1UWVgu8MbwbF7eNMjvWaR3/xKkzSp7m+thtpOsHTKlzEpuEMPPufkQF+7I5u4jb3l1JbmHZWb+/d+Mjw6eOnxDeXBPCj6f/MkRERMTt3vgpnQ+W7gBg4k1duLpLrMmJTi+3JJfMwkwsWM48EfxgBmQuASzQbYQn4kkNaRMTzKy7+9E4xI+tuYe59d2VZBecXdnoFXNkQriePHVKKhoiIiLiVpMXb+fNhdsAeP66jvyhV5zJic7s6KTe9hHtCfEJOf3BSR+7PiYMhrDa/7XJ6bWKCmLWPf1oGuZPRl4xw99dwZ780jO+r2dMTwC2HtrKobJDro1Ne4LFBoV7ID+rJmPXCSoaIiIi4jZTftnOy/M2A/DEFe0Z3T/e3EBnaXX2agD6NO5z+gMrKyD1U9frXmNrOJV4SotGgcy6px9xEf7sPFDC8HdWsOtgyWnf08i/EQmhCQAk57jWX8EnEGK7uV7vXF6DiesGFQ0RERFxi0mLtjFxrqtk/GlIG+69JMHkRGfv6PCXo+PuT2nzD1C8H4KbQNthHkgmntIsPIBZd/cnvlEAuw+VMvydFew8UHza91Stp3H88KkWF7g+Zi6tqah1hoqGiIiInLe3Fm7ln/O3APDI0Lb8aUhbkxOdvZziHHYW7sRqsZ75iVNJU10fu4+CMz0CV+qc2DB/Zt3Tn1ZRgewtKGP4OyvZkXfqsnG0aKzJXnNsY/yFro87l9Vk1DpBRUNERETOmWEYvP5TOq/+mA7Any9vxx8vbWNyqupZk+P6ITExIpFgn9Os8ZG3DXb8ChYr9BjtoXTiaTEhfsy8ux9tooPILixj+Dsr2L7/8O8ee3RCePqhdArKC1wbm/dz/R05mAGF+zwVu1ZS0RAREZFz4nAaPP3Nb7y5YCsAjw9rz4RBrU1OVX1Hfxt9xmFTR+9mtB6qSeD1XHSwH5/d3Y92McHkFpVz67sr2ZZbdNJxkf6RtAxtiYFxbD0Nv1Bo3Nn1uoHf1VDREBERkWorszt4YEYyn67KwmKBF67vxH0D686cjOOdVdGwl0HqDNdrTQJvECKDfPns7n60bxzM/iNlY2vOyWXj6F2NE4ZPtTgyfKqBz9NQ0RAREZFqKSixM3bqGub+lo2PzcqkET0Y1a+F2bHOSXZxNruKdmGz2OgRfZr5GWnfQ+lBCGnquqMhDUJEoA+f3dWPDk1CyDtcwW3vnVw2+jXpB8Dyvcc9ZSr+yIRw3dEQEREROTvbcou4btJSVmQcIMjXi4/G9ubKzk3MjnXOjv4WukOjDgT5BJ36wLUfuj72uANsXh5IJrVFeKAPM+7qS8fY3y8bfZv0xWqxklGQQXZxtmtj8/6ABfLS4XCuOcFrARUNEREROSsL0nK4ftJyMg+U0DTMn//e058BrSPNjnVejq6fcfTpQb8rNw2ylrsWYusxykPJpDYJC/Dh0/F9j7uzsaqqbIT6htIpshNw3F2NgAiI6eh63YDvaqhoiIiIyGk5nAb//nkr46et5XB5JX1bRvDdAxfQIfYMK2jXAVXzM2JOMz/j6N2MdldASKwHUkltdGLZKOe291ZVPY1qQOwA4H+GT1Wtp6GiISIiInKSXQddqyS/8XM6hgEj+zVn+vi+NAryNTvaedt7eC97Du9xzc841foZFcWwbqbrda9xngsntVJ4oKtsJB4pGyPeW0lmXjEXxLpKxYq9K3A4Ha6DNU9DRUNEREROZhgGXyXv5op/L2HtzkME+Xrx+h+68uL1nfG21Y8fH47ezejYqCOB3oG/f9CGL6C8EMJbQqtBHkwntdXRstE2JoicQlfZCLO2Itg7mMKKQjYd2OQ68OgdjdxNUHzAvMAmqh/fKURERMRttu8/zOgPV/PIf9dxuLySXi3CmfvQRdzYo5nZ0dzq6DCXvk36nvqgo8Omeo0Fq35sEpeIQB8+Hd+PhCMriI/8YC1dIl3zfJbtPXIHIzASotq7XmctP8WZ6jf9FyMiIiIAHC6v5KW5aQz7168s2ZqHj83KI0PbMvPufsRFBJgdz62chpMVe1cAx8bXn2RPMuxLBZsPdBvpuXBSJ0QF+zLjrn7ENwpg96FS1qVHA1T9vQIgvmGvp6Hns4mIiDRwBaV2PlmRyYfLMjlYXAHA4PbRPHt1B+IjTzGkqI5LO5jGofJDBHgF0DWq6+8ftPYD18cO10NgI49lk7ojJsSPGXf145YpK9ib04KgYFi3fx1FFUUE+wS7isaa9yHjF7OjmkJFQ0REpIHak1/KjFU7mbZ8J0XllQDENwrgmas7cGlijMnpatbR3zr3adIHb5v3yQeU5sOGL12ve9/puWBS58SG+TN9fF9umbKC0vJI8M1jya6VXJkwFFpeAlhgfxoU7oOQurvmzLlQ0RAREWlASisczN+YzRdJu1m2PQ/DcG1vEx3EhEGtubpLE7zqyWTv01m2xzWO/ujTgk6yfhZUlkJ0B4g7zRwOEaBlZCDTx/fhls/bgW8eL//yLYObD8YvIAJiu8HeFMhYDN1uMzuqR6loiIiI1HO7DpaweEsuCzfnsnz7AcornVX7+rWKYMyAllzWIQar1WJiSs8ptheTmpsKnKJoGMZxk8DHgaVhXBc5P+0bh/Dwhdfwxvpl5Dk28KeZKUy6vSe2hMGuorF9oYqGiIiI1F2GYbB9fzFrMw+yOvMgazIPsutg6QnHNI8I4KYezbixR9N6N8n7bKzJXkOlUUmzoGbEhcSdfEDWCti/GbwDoctwzweUOuvWToN4c4MX+Bzkx60bePZbX17sNhDLktdcdzQMo0EVVxUNERGROqzM7mDdrnxW7zhIctYhkrPyKSi1n3CMzWqhZ/NwBrWPZnD7aNrGBGFpQD/s/K+qYVNNTzFs6ujdjM43g1/dX/1cPCfAO4C+TfqwfO9yvII38emqaGICWvBH7wAozoWcjdC4k9kxPUZFQ0REpA4xDIMtOUX8tDGH5dsPkJx16IShUAC+Xla6xYXRp2UEveMj6NEinCBf/ZN/1NH1M373sbbFebDpW9frXmM9mErqi0ubX8ryvcuJb55B+oGBvL5oJzc160HTvKWQsUhFQ0RERGqXTXsL+XbdHub/lk3mgZIT9kUF+9KnZQS9WoTTs0U4iU1C6s3q3e62q2gXWUVZeFm86NO4z8kHpH4KjgqI7QGx3T0fUOq8QXGDeHHli+wrS2f8wAjeX3yQD7Nb8ozXUtc8jQEPmh3RY1Q0REREaim7w8n8jdlMW76T1ZkHq7b7eFm5uE0kA9tF0z+hEa0iAxv0UKjqOPpY2y5RXQjyCTpxp9MJa6e6Xvca5+FkUl9EBUTRJaoL6/avI6FFJrf27sivazuDFzgzl2G1l4G3n9kxPUJFQ0REpJapdDj5PGk3//55K9mFZQB4WS0M7RDD1V1iGdguikANhTonp52fsWMxHNoBvqHQ6UbPBpN65dLml7Ju/zoW7lrI29cPZ3x+KdlZ4TR2HCJn42Jiug0zO6JH6LuUiIhILWEYBovT9/PSnDTScw4DEBnky4i+zRnRpzmNQxvGb0Frit1hZ1X2KuAUj7U9Ogm8663gUz9XRBfPGNx8MK8nvc7a7LWUVBYxaWRPVr7WncYVC1k4exbD2gwmPNDH7Jg1TgM4RUREaoHsgjLGfrSGsVPXkJ5zmLAAb569ugPLnhjEI0PbqmS4wdqctRTbi2nk14jERokn7izcB5vnuF5rEricpxYhLWgd1ppKo5Jfd/9KkK8XvS+9CYDO5cncMz2J8kqHySlrnoqGiIiIyWav38fl//qVxVv242Ozcs/Frfjlz4MYd2FLfL1sZserNxbvWgzAwLiBWC3/8yNQyidgOKD5AIhOPOm9ItU1uPlgABZkLQAgtONlAHSyZrJtRyZPfvUbhmGYls8TVDRERERMcri8kkf+m8qEGckUlNrp3DSUOQ9dxF+uTCTU39vsePWKYRgs2rUIcBWNEzgqIekj12tNAhc3ubT5pYBrXlBpZSkERUNMZwAGea3ny+TdvL14u5kRa5yKhoiIiAl2Hyrh5snL+Sp5D1YLPDCoNV/eN4DW0UFnfrNUW/qhdPYV78PP5kffJn1P3Ln1RyjcAwGNoMO15gSUeicxIpHYwFjKHGVVTzuj7eUAPNjMVTD+OX8Ls9fvMytijVPREBER8bCUrENcP2k5m7OLiAr2ZdY9/Xns8nb4eOmf5Zpy9G5Gv9h++Hv5n7jz6CTwbreDl6+Hk0l9ZbFYqoZP/bzzZ9fGdlcAEH9oBXf2bwrAo5+n8tueAlMy1jR9RxMREfGgH9bvZfi7K8k7XE5ikxC+nXABveMjzI5V7x2dnzE4bvCJOw5lwrYjPwT2HOPBRNIQXB7vuoPxc9bPlNhLXAtBBkZBeSFPdspnYLsoyuxO7pq2ltyiMpPTup+KhoiIiIfMXJ3Fg5+lUFHpZEhiNF/c25/YMP8zv1HOS05xDhsPbMSChYuaXXTizqSPAQNaDYJGCabkk/qra1RX4oLjKK0sdU0Kt1qhjat82LbO583bupMQFci+gjLu+SSJMnv9ehKVioaIiIgHfLJyJ098tQHDgNH9W/DOqF5adM9Dftn9C+BaDTzSP/LYjsoK19OmQJPApUZYLBauSbgGgO+3f+/a2O7IYn1b5hLi68X7d/Qm1N+blKx8nvxqQ716EpWKhoiISA2bumwHz3zzGwDjL2zJ367tiM1qMTlVw3HKp01t/h6K90NQ46qx8yLudnWrqwFYuW8lOcU5rrtnNh/XKvR5W2kZGcikET2wWS18lbKH95fsMDmx+6hoiIiI1KCpy3bwt+83AXDvJQk8dVUiFotKhqeU2EtYtc+1GvhJ8zPWfOD62GM02PQ4YakZccFx9IjugYHB7B2zwTcI4o8M4UufC8CFbSJ5+irX+i0vzU1jydb9ZsV1KxUNERGRGvJNyp6qkvHg4NY8PqydSoaHLd+7HLvTTvPg5rQMbXlsR85G2LkMLDatBC417toE12OTv9v2nWtoVNsjw6fS51cdM2ZAPDf3bIbTgAdmpJB1oMSMqG6loiEiIlIDFm3J5bHP1wEw9oJ4HhnaViXDBD/t/AlwDZs64fqved/1sf1VEBJrQjJpSC6Lvwwfqw/bC7az6eCmY/M0slZCyUHANZ/jxes70TUujIJSO3d/spbi8koTU58/FQ0RERE3S9p5iPumJ1HpNLiuWyzPXNVBJcMEpZWlVY+1PfqYUQDKCmDdLNfrPnd5PJc0PME+wVVrany//XsIaw7RHcFwHHu8MuDnbeOdkT2JDPJlc3YRf/5iXZ2eHK6iISIi4kYZ+w8z7qM1lNmdXNI2in/e3BWrJn6bYsnuJZRUltA0qCmdIzsf27FuJtiLIar9sbHyIjXs6NOn5mTMwe60V60Szpa5JxzXONSPKSN74G2zMGdDNu/8muHpqG6joiEiIuImh4orGPfRGgpK7XSNC2PyyB5a7dtE8zLnAa67GVV3lAzj2LCp3uNBd5rEQwbEDqCRXyMOlR9iUdYi17A9gK0/gf3Exfp6xUfw7DUdAXhl3uY6Ozlc3/1ERETcoKLSyb3Tk8g8UELTMH/eH92LAB+tk2GWYnsxv+7+FYArWh736Nodv0JeOvgEQZfhJqWThsjL6sWNbW4EYMbmGdC0J4Q0g4oi2L7gpONH9m3OLUcmhz/4WQq7Dta9yeEqGiIiIufJMAye+noDq3YcJMjXiw/H9CYq2NfsWA3aol2LKHeUEx8ST7vwdsd2rHnP9bHLcPALMSecNFjD2w3HZrGRlJPElkPp0OE6146NX590rMVi4YXrO9GlWSj5JXbu+SSJ0oq6tXK4ioaIiMh5evfXDD5P2o3VAm+N6E67xsFmR2rw5u1wDZsa1nLYsWFT+VmweY7rde/xJiWThiwmMIahLYYCR+5qdLzetWPLvJOGT4FrcvjkkT2JCPRh075Cnvqmbq0crqIhIiJyHhZvyWXivM0APHt1Bwa2izY5kRSUF7Bs7zIAhsUPO7Zj1Tuup/y0GggxHcwJJw3eiMQRAMzOmE1+ZGsIaXrK4VMATcP8eWtEd6wW+Cp5D9NX7vRk3POioiEiInKOduQV88fPUjAMuK1PHHcMiDc7kgALshZQ6aykbXhbEsISXBvLCiF5mut1vwnmhZMGr1tUNxIjEil3lPPltq+hw/WuHRu/OeV7BiRE8sQV7QF4/odNJO08VPNB3UBFQ0RE5BwcLq/k7mlrKSyrpEfzMP56bUetlVFLVA2bOv5uRsp0KC+EyLbQeohJyURccy9uT7wdgJlbZlKZ6HrsLVvm/u7wqaPuuqgVV3Vugt1hcP+nSeQWnfrY2kJFQ0REpJqcToNH/5vK1tzDxIT4MmVkT3y9bGbHEiCvNI9V2auA44qG0wGrJrte97sfrPrxR8w1rOUwwn3DyS7OZrFRdMbhU+AqKC/f3IXW0UHkFJbzwIwU7A6nB1NXn/5LExERqaa3F29j/sYcfGxWpozsSXSIn9mR5Ihvtn2D03DSNaorcSFxro2bf3BNBPePgK63mhtQBPC1+XJz25sBmJY2HSPxWteO0wyfAgjy9eKdUT0J8vVi9Y6DvDx3cw0nPT8qGiIiItXwS/p+XvspHYAXru9I9+bhJieSowzD4KutXwFwU5ubju1YMcn1sfed4O1vQjKRk93a/lZ8rD6k5KawNjbRtfEMw6cAEqKCePWWrgC8v3QH36/bW9NRz5mKhoiIyFnadbDkuMnfzRneu7nZkeQ4a3PWsqtoF4HegVwef7lr4+61sGsV2Hyg913mBhQ5TnRANDe1dRXiKft+OTZ8attPZ3zvsE6NuW+g60EHj3+5nvScohrNeq5UNERERM5Cmd3BPZ8kUVBqp2tcGH+9Vo9HrW2+3Pol4FoJPMA7wLVx6Ruuj51uhuAYk5KJ/L5xncbhbfVmdc4aktpe4tq4buZZvffRoW25oHUjSioc3PtJEoVl9hpMem5UNERERM7AtfL3b2zaV0ijQB8m395Dk79rmYLyAn7KdP0muGrYVPYG1/wMLHDhn0zLJnIqjQMbc0PrGwB4h0LXxvR5UJx3xvd62ay8eWt3YkP9yMgr5rH/rsPprF2L+aloiIiInMH0VVl8mexa+fs/I7oTG6Zx/rXNDxk/UOGsoG14Wzo26uja+Msrro8db4CoduaFEzmNOzvfiZfFixUH1pMa2xGclbDhi7N6b6MgXyaP7ImPzcqPm3KY/Mv2Gk5bPSoaIiIip5GcdYjnv98IwBNXtGdAQqTJieR/GYZRNWzqpjY3udYzydkIad8BFrjk/8wNKHIasUGxXNf6OgCmRBx5uETqp2f9/q5xYTx/natcv/rjFn5N3+/2jOdKRUNEROQU9heVc//0ZOwOgys7N+aui1qZHUl+x8YDG9l6aCs+Vh+uanWVa+PRuxkdroPoRPPCiZyFOzvfic1iY1lxFuv8AiB7PWT/dtbvv7VPc27tHYdhwB9nprDrYEkNpj17KhoiIiK/o9Lh5MHPkskuLCMhKpBXbu6qlb9rqc/TPwdgaPxQQn1DITcNNn3r2qm7GVIHxAXHcW2Cay2N12NbYACs+6xa5/jrtR3p0iyU/BI7905PoszucH/QalLREBER+R0T525mZcZBAn1svDOqF0G+XmZHkt+RV5rH99u/B+APbf/g2vjLK4ABiddCTEfzwolUw4RuE/Cz+ZFsFLMwwB/WzwLH2T9Jys/bxuSRPYkI9GHj3kKe/HoDhmHu5HAVDRERkf/xbeoe3l+6A4BXb+lK6+ggkxPJqcxIm4HdaadrVFe6R3eHfetg49eunZc8bm44kWqICYzhjo53APB6o0bYi/fDtp+rdY6mYf68NaI7NquFr5L38PHyzBpIevZUNERERI6Ttq+Qx79cD8D9AxO4onMTkxPJqRTbi5m5xbXmwNiOY7EAzPk/wHCtm9G4k5nxRKptbKexNPJrRJaXlVkhwdWaFH7UgIRI/nJFewBemJ3GyowD7o551lQ0REREjsgvqeCeT5Ioszu5uG0Uj16mR6LWZl9t/YqiiiLiQ+IZGDfQ9UjQXSvBOwCGPm92PJFqC/QOZEL3CQBMCQuhYOs8KNxX7fPceWFLrusWi8NpMOHTZPbml7o76llR0RAREQEcToOHZqaSdbCEuAh/3ry1GzarJn/XVnannWmbpgEwuuNobPZS+OlZ186LHoHQpiamEzl3N7S+gdZhrSmw2XgvJBCSplb7HBaLhYk3diGxSQgHiitMmxyuoiEiIgK8Mm8zv6Tvx8/byjsjexEW4GN2JDmN+ZnzyS7OJsIvwvW0nqWvQ9FeCGsB/R80O57IOfOyevFIz0cA+DQkmG0pH0FlRbXP4+9j491RPQkP8Gb97gL+8pXnJ4eraIiISIP3dcpu3vk1A4B/3tyVDrEhJieS0zEMg6m/uX7Le3vi7fgW7IHl/3HtvPwf4O1nYjqR83dh0wsZ2PRiKi0WXgg0MI4+4KCa4iICmHR7D2xWC1+n7OH9JTvcnPT0VDRERKRBW787n8e/3ADAhEEJXNM11uREciaLdi0i/VA6/l7+DG9zC/zwMDgqoNVAaH+V2fFEzpvFYuEv/Z7C32Ij2c+Pb9e+ec7nGpAQybNXdwDgpblp/OLBlcNVNEREpMHKLSzj7mlJVFQ6GZIYzaNDNfm7trM77byR9AbgupsRunYqZCx2TQC/8lXQoopST8QGxXJfx3EAvGY5RP6OX8/5XKP7t+DW3nE4DXhgRjLb9x92V8zTUtEQEZEGqbTCwV2fJJFdWEbr6CDeGN4NqyZ/13pfpH9BZmEmEX4R3BneDRa+6NpxxSsQ2cbUbCLuNrL7fbSxBpBvs/H6ir+d83ksFgt/u64jPVuEU1RWyfiP15JfUv15H9WloiEiIg2O02nw6OeprNuVT1iAN++P7kWwn7fZseQMiiqKmJw6GYD7Oowh6JsHwHBAp5ug+0iT04m4n7fVm2e7/wmAr+25rN1RvQX8jufrZeOdUT1pGubPjrxi7v80GbvD6aakv09FQ0REGpxXf9zCnA3ZeNssvDuqF/GRgWZHkrPwwYYPOFR+iJahLblp0yIoyHI9ZerqNzRkSuqtbp1u42aH6wEHzyx/hhJ7yTmfKzLIlw/G9CLQx8by7Qd49tuNNfokKhUNERFpUD5fu4u3F28HYOKNXejTMsLkRHI29h3exyebPgHgEUsU3mnfgtULbp4KfqEmpxOpWY/2fJgmlZXsrjzM66teOq9ztW8cwpu3dcdigc9WZ/HB0pp7EpWKhoiINBhLtu7nya9dT5h6cHBrburZzOREcrbeSHqDCmcFvX2iuCTlC9fGq16DZj3NDSbiAUGdb+V5u+vO66zt37B8z/LzOt+liTE8dWUiAH+fk8a837LPO+PvUdEQEZEG4bc9Bdz7SRJ2h8E1XWN5eEhbsyPJWZqfOZ+5mXOxYuGxHeuxgOsJUz3HmJxMxEOsVvpd+BduKygC4Nllz1BYUXhep7zzwpbc3rc5hgEPzUwhOeuQO5KeQEVDRETqvawDJYyZupriCgcDEhrx6i1d9ISpOmJ/yX5eWPECAOMP5dOhwg7DJkKfu0xOJuJhHa7nT9Yomtvt5JTm8vLql8/rdBaLhb9d25HB7aMpr3Qy/uO1ZOYVuymsi4qGiIjUawcOlzP6w1XkHa4gsUkI74zqia+XzexYchYMw+DZxY9RUFFAYnkF9+YXwGUvQr/7zI4m4nlWGwGXPM7f9x/Aahh8t/07ZmfMPq9Tetms/Oe27nRuGsrB4grGTF3NgcPlbgqsoiEiIvVYQamd0R+uJvNACc3C/fl4bG89xrauMAw+X/Bnlu5Pxsdp8FJhOd63fAQDHjQ7mYh5Ot5At+B47s53DZt6fsXz7CzceV6nDPT14oMxvWga5k/mgRLGTF1DUZndHWlVNEREpH4qLq9k7NTVbNxbSKNAHz4e14foED+zY8mZOJ2Q9j0Z7w7g1V1zAXiIcBLuWgodbzA5nIjJrDa4+M/cm19Ar/JKSipLeOyXxyh3nN9diOhgP6bd2YeIQB827Cng7mlJlNkd5x/3vM8gIiJSy5TZHYz/eC3JWfmE+nszfXxfEqKCzI4lp1N+GFI/gykXkvfFaO6z5VNqtdLHrzEjRy+EUD0hTASATjdhi+7AxJwcwi3ebD64mdfWvnbep02ICuLjsX0I8vViRcYBHvwshcrzXNBPRUNEROqV8koH905PYkXGAYJ8vZg2rg+JTULMjiW/x1EJ2xfCV/fAq23hm3sp2b+J+xs3Zq+3Fy2CmvHqdf/FatNwN5EqVhtc+SoxDgd/37cXgM82f8aPmT+e96k7NwvlvdG98PGy8tOmHP7vy/U4nee+oJ/FqMnlAEVERDyotMLB3Z+sZcnWPPy8rUwb11cL8tU2TgfsXA4bv4a076B4f9WuyoiWPNi4MUtL9xDhF8H0K6YTFxJnYlg5leLiYoKCXHcJDx8+TGBgoMmJGqAv74IN/+X15u2YaivF38ufT6/8lDbhbc771D9uzOa+T5NxOA1u6dmMl286tyf1qWiIiEi9UFxeyZ0fr2FlxkH8vW18MKYXAxIizY4lRx3YDqmfQuoMKNp3bLt/OHS8kcrOf+C5rO/4LuN7/Gx+fHj5h3SO6mxeXjktFY1aoCgb/tOLyooi7u10IauKs2gW1IyZV88k1Df0vE///bq9PDQzBacBf+jVjIk3Vr9sqGiIiEidV1hmZ8yHq0nOyifI14uPxvamV7zuZJjO6YTN38Oqd2DnsmPb/cIg8WroeCO0vJgSp53HfnmMJXuWYLVY+dfAfzGo+SDTYsuZqWjUEismwfwnyQ+I4NaWrdlTkk3/Jv15e8jbeFm9zvv0363by5+OlI3hveJ46cbO1SobKhoiIlKn5RaWMWbqGjbtKyTEz4tpd/alW1yY2bEatsoKWD8Llv0bDmx1bbNYIeFS6D4S2l0BXr6Aa0G+CQsmkHYwDT+bHxMvnsilzS81MbycDRWNWsJhhykXwf40tnS4ilH2rZRWljGm4xge7fWoWz7Ft6l7eHhWKk4DbujelFdu7oK37eymeatoiIhInbUtt4g7PlzDnvxSIoNcj7DtGHv+QwbkHDmd8NsXsOB5KNjl2uYXCr3vgl7jILTpCYdvzNvII4sfYW/xXiL8IvjP4P/QJaqLCcGlulQ0apGsVTD1CjAczL/kjzyW9Q0Az/Z/llva3uKWT/Ft6h4e+e86HE6Dwe2jmTSiB/4+Z174VEVDRETqrC5/nU9hWSUtIwP5eGwfmjcKMDtSw5W5DH58CvamuP4c1Bj6T4CeY8DvxKd+2R12pqyfwgcbPsBhOGgR0oLJl07WxO86REWjlvn1VVj4AngHMGngvUzZ9gVWi5U3B73JJXGXuOVTLNycw/2fJlNmd9KrRThf3DfgjO/R421FRKTOKiyrpEfzML68b4BKhlkK98HnY+CjK10lwycYLn0WHkqFC/54UslIO5DGrbNv5d317+IwHFwRf4WeLiVyvi58BFoNAnsJ96+by/Utr8ZpOPnzr39mw/4NbvkUg9vHMP3OvoT4ebF256Gzeo/uaIiISJ11zydr+fet3fHzPvMtfHEzpwPWvA8LXoCKItccjJ5jYOBfICj6pMN3Fe3i7dS3mZ0xGwODcN9wnu73NJfFX+b57HLedEejFjqcC5MvgOJc7N1H8mCgg2V7lhHuG870K6fTPKS5Wz7N5uxCRn+wmtVPDTnjsSoaIiJSZzmcBrZzeLa7nKe9qfD9Q7Av1fXnpr3g6jegycnzK/Ye3suHv33Il+lfUmlUAnBF/BU83udxGvk38lxmcSsVjVoqYzFMux4wKBn0JGMKk0g7mEaTwCZ8ePmHNAtu5pZPs+tgCXERZ76LrKIhIiIiZ6eiGBb9A1a+DYbTNdF7yF+hxxiwHhuNbRgGa3PWMiNtBgt3LcRpOAG4IPYCHuzxIB0bdTQnv7iNikYttvw/8OPTAORd9jfG5iwgszCT2MBYPhz2IU2Dmp7hBO6joiEiIiJntvVnmP0w5Ge5/tzxRhg2EYJjqg45UHqA2Rmz+Wb7N2w9tLVqe98mfbmnyz30btzb06mlhqho1HILXoAlrwKQe/Wr3Lnru6qyMXXYVGKDYj0SQ0VDRERETq1wL8z7C2z6xvXn0Di46jVoezkAZZVl/Lr7V37I+IElu5dUDY/ys/lxTcI1jGg/gtbhrU0KLzVFRaOWMwyY+zisfgcsNnKveY1xO79kZ+FOmgY1ZcqQKcSHxtd4DBUNEREROZmjEta8Bwv/fmSytw363guDnqTS24/V+1Yze8dsFmQtoNheXPW2zpGduS7hOoa1HEaor9Y0qa9UNOoApxO+ewBSPwUg58I/cWdhEjuLdhLqG8q/Bv6LXo171WgEFQ0RERE50bafYf5TsH+z68/NeuO46jWSLRXM3TGXn3f+zKHyY4+3bBLYhGEth3Ftq2t196KBUNGoI5wO13yNlW8DkJd4FQ8FOll/YCNeVi+eH/A81yRcU2OfXkVDREREXHI3w0/PwNYfAXD6R5DSbxzzfa38lPUzeaV5VYdG+EUwtMVQrmx5Jd2iu2G1aGmuhkRFo45JngY/PAJOO2WNO/Nky0R+yl4JwLhO43ig2wN427zd/mlVNERERBq6vG3wy8uw4XMcGKT4B/BTq978bBSRe1y5CPYJZmiLoVwefzl9GvfBy+plYmgxk4pGHbRzOcwaCSUHcFq9+XenwXxYlAZAYkQiL130EglhCW79lCoaIiIiDVXOJlj+Jvb1s1jj68PCQH8WhISTR2XVIcHewQxqPohh8cPo16RfjfzWU+oeFY06qmAPzH4U0ucC8GNMK54P8aGgsgQfqw8P9XiIEYkj3PZLBBUNERGRhsTpgPT5FK56i+W5KfwS4M8v/v4U2Y4NfQr2CWZw3GAui7+Mfk364WPzMTGw1EYqGnWYYcCmb2Hu/8HhHPbbrDzbrBVLrRUAxIfEM6HbBC6Lv+y8h0SqaIiIiNR3hkHlniTSUqeyKmsxS60VpPr54rAcW1U9wi+CQXGDGNJiCH0b99WdCzktFY16oPQQ/PIKrJ2KUVnKF8GBvNmoEflHvi20C2/H+M7jGRg3ED8vv3P6FCoaIiIi9VBRwS42pn3Jb7uXkJq/lSSbg8PWE3872SoojotbXMqguEF0jeqKzWozKa3UNSoa9cjh/bByEqx+j8P2YqaHBvNxaEjV94sgL3+Gxg/jqlZX0T26e7XucKpoiIiI1FHljnJy8zPJzktjb94mMvI2knF4FzsqCthpdZ50fDA2eoe3o2+rq7i4xWCaBTczIbXUByoa9VDpIVj/OWz4nIK9a/kkJJjvggPZ53VsvoYPVjoFNqN7dDf+dPHfz3hKFQ0REamzPp17v9kRzpJR9f9H/9k1MFxbDQMnThyGE8MwqDQcVBqVVDodVDorKXfaKXVUUOasoNhZQaGjnELDTj4OCiyn/IQANHVa6OwXTafo7vTucCvtYrrproW4hYpGPXdwB/z2Jc4di0nOXccPfl4sDPTnkO3Y948Nd2w442lUNEREpM7q/HFnsyOYzs/pJMZp0NjiQ7xvBK1CW9IqqgttW11GRFSi2fGknlLRaEAqK2BvMkbWKnbmriMlP53U8v387b4tZ3yrioaIiNRZj02/2OwIJzjdDQbL/+y1HNlmAawWC1asWC1gtVjxttjwstjwsljxt/niZ/PDz8sXf68AQv0iCA2IJMQ/iqjwBEIatcXiH1qTX5bISVQ0GjjDAMsZbqmioiEiIiIi1aSiIWfj/B6OKyIiIiIi8jtUNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO1UNERERERExO0shmEYZocQERERkbrDMAxKSkoACAgIwGKxmJxIaiMVDRERERERcTsNnRIREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbdT0RAREREREbfzMjuAiIjIuTAMg6KiIrNjiIg0WMHBwVgsllPuV9EQEZE6qaioiNDQULNjiIg0WAUFBYSEhJxyv8UwDMODeURERNxCdzROVFhYSFxcHLt27TrtP/wNla7P6en6nJ6uz+/THQ0REamXLBaL/sH/HSEhIboup6Hrc3q6Pqen61M9mgwuIiIiIiJup6IhIiIiIiJup6IhIiJSD/j6+vLcc8/h6+trdpRaSdfn9HR9Tk/X59xoMriIiIiIiLid7miIiIiIiIjbqWiIiIiIiIjbqWiIiIiIiIjbqWiIiIiIiIjbqWiIiIjUEZMmTSI+Ph4/Pz/69u3L6tWrT3nse++9x0UXXUR4eDjh4eEMGTLktMfXB9W5PsebOXMmFouF66+/vmYDmqy61yc/P58JEybQpEkTfH19adu2LXPmzPFQWs+r7vX517/+Rbt27fD39ycuLo6HH36YsrIyD6WtG1Q0RERE6oBZs2bxyCOP8Nxzz5GcnEzXrl25/PLLyc3N/d3jFy9ezG233caiRYtYsWIFcXFxXHbZZezZs8fDyT2jutfnqMzMTB577DEuuugiDyU1R3WvT0VFBUOHDiUzM5MvvviCLVu28N5779G0aVMPJ/eM6l6fGTNm8MQTT/Dcc8+RlpbGBx98wKxZs3jyySc9nLx20+NtRURE6oC+ffvSu3dv3nrrLQCcTidxcXE8+OCDPPHEE2d8v8PhIDw8nLfeeovRo0fXdFyPO5fr43A4uPjiixk3bhxLliwhPz+fb775xoOpPae612fKlCn885//ZPPmzXh7e3s6rsdV9/o88MADpKWlsWDBgqptjz76KKtWrWLp0qUey13b6Y6GiIhILVdRUUFSUhJDhgyp2ma1WhkyZAgrVqw4q3OUlJRgt9uJiIioqZimOdfr8/zzzxMdHc2dd97piZimOZfr891339G/f38mTJhATEwMnTp14h//+AcOh8NTsT3mXK7PgAEDSEpKqhpelZGRwZw5c7jyyis9krmu8DI7gIiIiJxeXl4eDoeDmJiYE7bHxMSwefPmszrH448/Tmxs7Ak/TNUX53J9li5dygcffEBqaqoHEprrXK5PRkYGCxcu5Pbbb2fOnDls27aN+++/H7vdznPPPeeJ2B5zLtdnxIgR5OXlceGFF2IYBpWVldx7770aOvU/dEdDRESknps4cSIzZ87k66+/xs/Pz+w4pisqKmLUqFG89957REZGmh2nVnI6nURHR/Puu+/Ss2dPhg8fzlNPPcWUKVPMjlYrLF68mH/84x+8/fbbJCcn89VXXzF79mxeeOEFs6PVKrqjISIiUstFRkZis9nIyck5YXtOTg6NGzc+7XtfffVVJk6cyM8//0yXLl1qMqZpqnt9tm/fTmZmJtdcc03VNqfTCYCXlxdbtmwhISGhZkN70Ln8/WnSpAne3t7YbLaqbYmJiWRnZ1NRUYGPj0+NZvakc7k+zzzzDKNGjWL8+PEAdO7cmeLiYu6++26eeuoprFb9Lh90R0NERKTW8/HxoWfPnidMPHU6nSxYsID+/fuf8n2vvPIKL7zwAvPmzaNXr16eiGqK6l6f9u3bs2HDBlJTU6v+d+211zJo0CBSU1OJi4vzZPwady5/fy644AK2bdtWVcAA0tPTadKkSb0qGXBu16ekpOSkMnG0lOk5S8cxREREpNabOXOm4evra3z00UfGpk2bjLvvvtsICwszsrOzDcMwjFGjRhlPPPFE1fETJ040fHx8jC+++MLYt29f1f+KiorM+hJqVHWvz/+64447jOuuu85DaT2vutcnKyvLCA4ONh544AFjy5Ytxg8//GBER0cbL774ollfQo2q7vV57rnnjODgYOOzzz4zMjIyjB9//NFISEgw/vCHP5j1JdRKGjolIiJSBwwfPpz9+/fz7LPPkp2dTbdu3Zg3b17VBNasrKwTfsM6efJkKioquPnmm084z3PPPcdf//pXT0b3iOpen4amutcnLi6O+fPn8/DDD9OlSxeaNm3KQw89xOOPP27Wl1Cjqnt9nn76aSwWC08//TR79uwhKiqKa665hr///e9mfQm1ktbREBERERERt2u41V5ERERERGqMioaIiIiIiLidioaIiIiIiLidioaIiIiIiLidioaIiIiIiLidioaIiIiIiLidioaIiIiIiLidioaIiIiIiLidioaIiIiIiLidioaIiIiIhy1dupQ+ffrg5+dHZGQk//73v82OJOJ2KhoiIiIiHjRnzhxuuOEG7r//ftavX88999zDww8/TGZmptnRRNzKYhiGYXYIERERkYagrKyMNm3a8PLLLzNixAgAHA4HYWFhTJo0idGjR5ucUMR9dEdDRERExEMWLlxIaWkpw4cPr9pms9mwWCz4+vqamEzE/VQ0RERERDxk0aJFdOvWDZvNVrVt27ZtFBUV0b17dxOTibifioaIiIiIh6SkpFBRUXHCtrfffpuePXvStm1bk1KJ1AwvswOIiIiINBQpKSkYhsG0adPo27cvn3/+OZMnT2b58uVmRxNxOxUNEREREQ/Iysri4MGD/PDDDzzxxBOkp6fTpUsX5s2bp2FTUi/pqVMiIiIiHvDdd98xduxYDhw4YHYUEY/QHA0RERERD0hJSaFz585mxxDxGBUNEREREQ9ISUmhS5cuZscQ8RgNnRIREREREbfTHQ0REREREXE7FQ0REREREXE7FQ0REREREXE7FQ0REREREXE7FQ0REREREXE7FQ0REREREXE7FQ0REREREXE7FQ0REREREXE7FQ0REREREXE7FQ0REREREXE7FQ0REREREXG7/wcpPTUBJLEcPgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally, we can compare the ground truth conditional posterior with the MNLE-conditional posterior.\n",
    "fig, ax = pairplot(\n",
    "    [\n",
    "        prior.sample((1000,)),\n",
    "        true_posterior_samples,\n",
    "        conditional_samples,\n",
    "    ],\n",
    "    points=theta_o,\n",
    "    diag=\"kde\",\n",
    "    upper=\"contour\",\n",
    "    kde_offdiag=dict(bins=50),\n",
    "    kde_diag=dict(bins=100),\n",
    "    contour_offdiag=dict(levels=[0.95]),\n",
    "    points_colors=[\"k\"],\n",
    "    points_offdiag=dict(marker=\"*\", markersize=10),\n",
    "    labels=[r\"$\\beta$\", r\"$\\rho$\"],\n",
    ")\n",
    "\n",
    "plt.sca(ax[1, 1])\n",
    "plt.legend(\n",
    "    [\"Prior\", \"Reference\", \"MNLE\", r\"$\\theta_o$\"],\n",
    "    frameon=False,\n",
    "    fontsize=12,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They match accurately, showing that we can indeed post-hoc condition the trained MNLE likelihood on different experimental conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('sbi')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  },
  "vscode": {
   "interpreter": {
    "hash": "9ef9b53a5ce850816b9705a866e49207a37a04a71269aa157d9f9ab944ea42bf"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
